M.S. IN GIST THESES

Wildland fires ravage the United States every year, and Community Wildfire Protection Plans (CWPP) are one of the main tools used to plan for and mitigate them. CWPPs are a community planning document that outlines the wildfire risk in the relevant jurisdiction and lays out plans on how to mitigate these risks. Currently there is not a standardized, national dataset for documenting them, which is the gap this thesis seeks to fill. The focus of this work is the development of a web geographic information system application and corresponding database, which collects and stores CWPP documentation and maps the extent of CWPP jurisdictions. The project is supported by volunteered geographic information (VGI) input by any users that take the initiative to do so. An automated data pipeline has been developed to expedite the confirmation of user-inputted data accuracy, and a PostgreSQL database has been developed to house all the CWPP data, which came from both an existing Excel Workbook. A prior version of this application was an Esri Storymap hosted on the Fire Adapted Communities Learning Network (FACNET) website. This project developed a new web application, from the ground up using hypertext markup language, cascading style sheets, and JavaScript. Overall, this work expands the availability of national CWPP information for planners and stakeholders, so civilians that wish to view their jurisdiction’s plans can do so, and those responsible for writing new CWPPs can easily view historical and existing plans and other resources. The project currently contains CWPPs for 11 western states but its coverage will grow as VGI is inputted via the CWPP Repository. It will be managed by the writer of this thesis and hosted by FACNET. The completed web application can be found at https://cwpp-repository.com/, and the GitHub repository for it can be found at https://github.com/SpatialSolutions93/CWPP-DatabaseWebsite/tree/main/WebApp.

Cities have prioritized the utilization of trees and the urban tree canopy (UTC) due to their associated benefits of cooling, atmospheric carbon sequestration, and runoff water interception, among others. However, conditions of inequitable canopy coverage are inherent. Tree planting initiatives, like the City of Los Angeles’s Green New Deal, attempt to tackle this inequity through the identification of regions with the greatest lack of, and therefore greatest need for the UTC. Remote sensing and GIS are necessary tools for the city managers to monitor the urban forests and conduct temporal comparisons. Thus, this analysis compared pixel-based, objectbased, deep learning, and data fusion image classification approaches in identifying urban tree canopy using very-high resolution multispectral imagery, one-meter resolution LiDAR point clouds, and vector data. An accuracy assessment was conducted to compare each classification method according to type I error, type II error, and classification agreement with ground truth data. A final decision on best classification method is predicated on accuracy as well as methodological complexity, time, and replicability. The data fusion classification provides the result with the best accuracy results across both land cover classes despite its long geoprocessing time. The pixel-based, object-based, and deep learning classifications were unable to produce adequate classification accuracies regardless of improved geoprocessing times. Future analyses may look to automate similar data fusion classifications to produce similarly high classification results at a larger scale.

The rate of homelessness in the U.S. has steadily risen since 2016, prompting a focused effort to eradicate this crisis primarily through indoor shelters and permanent, affordable housing solutions. However, many unhoused individuals continue to camp nightly in various self-selected locations, lacking the basic necessities for habitation and doing so contrary to official public policy. Despite the inherent dangers and discomfort of outdoor living, some chronically unhoused individuals prefer it to traditional housing options. Emergency shelters present barriers to entry based on lifestyle, often don’t meet the desire of unhoused individuals for a sense of community and belonging, and have proven inadequate in meeting public health mandates, such as those required during the COVID-19 pandemic. As an alternative, various forms of outdoor housing encampments for chronically unhoused adults have emerged, particularly in U.S. cities on the West Coast. This project focuses on identifying suitable sites within Long Beach, California for such an encampment, capable of providing potential residents with access to basic necessities including potable water and sanitation. Sites within walking distance of essential services (e.g., food assistance, health clinics) are evaluated for their suitability using a method of analysis known as weighted overlay where the weights are based on the preferences of the unhoused population, supported by empirical studies for justification. Additionally, a sensitivity analysis is conducted to account for parcels within 2,000 feet of schools and parks that are subject to heightened scrutiny due to legal and safety concerns. The project must balance community norms with the needs of the unhoused population. The "Not in My Backyard" (NIMBY) mindset often opposes initiatives that disrupt established norms or introduce locally undesirable land uses (LULUs). By re-imagining outdoor sheltering options and incorporating xi insights from community dynamics, this project aims to offer more effective and compassionate solutions for the unhoused in Long Beach, California.

Geographic Local Routing for Social Connection (GLRSC) is an innovative and novel application which integrates road routing algorithms and multi-user digital environments to facilitate immediate and thematic social networking opportunities for users within a walkable reach and a 30-minute period. In 2023, the U.S. Department of Health and Human Services declared loneliness as a public health epidemic; the declaration demands research and innovative action to help Americans connect with one another through face-to-face contact. This thesis develops GLRSC-System-1, a deployed full-stack system that integrates GLRSC and introduces the application online. GLRSC-System-1 helps users increase their opportunities for social encounters by providing a meeting request system where users can request thematic meetings with other users within a 2km neighborhood. The system calculates an optimal midpoint for active participants based on their geolocation and synchronously routes the users to meet at an optimal midpoint within a 15-minute period. The thesis introduces the essential components required of any GLRSC system through the phase planning methodology of GLRSC-System-1. The essential components/phases for a base-level GLRSC system are a multi-user environment, system deployment, a meeting service, and a routing service. This project demonstrates the feasibility of implementing GLRSC in online systems and serves as a guide for the future development of GLRSC technologies

Recreational Off-road Adventure Motorcycle Mapping System (ROAMS) is a novel application created to facilitate motorcyclists’ enjoyment of nature, friends, and adventure. Currently there are limited route planning tools developed specifically for adventure motorcyclists. The developer of this application strives to apply relevant criteria and the needs of the emergent market of adventure motorcycling to this application. ROAMS is a web-based application which provides the ability to explore and locate user submitted routes that contain paved and unpaved roads and motorcycle permitted trails. Dynamic environmental events layers keep motorcyclists safe by identifying risks related to inclement weather, fire, and air quality. Points of interest such as camp sites, parks, and emergency services assist the rider in planning their experience. The application was designed to streamline the planning process and keep people safe by displaying which trails may be closed, what types of terrain may be encountered, and where weather events are occurring. In addition to enhancing the motorcyclist’s experience, the byproduct of this application protects the environment by keeping riders on designated trails, away from fragile ecosystems. All routes were developed in ArcGIS Pro to allow the user the ability to filter by difficulty or seasonal closures in the web-application. This application was created using ArcGIS Online’s Experience Builder and hosted in a GitHub page. ROAMS considers the motivations for motorcycle travel and incorporates these needs into a functional application; however, there is significant room for improvement in automation, data management, and interpolation. Future developments include the expansion of the pilot area, formalized user group testing, quality control, and network development.

Recidivism rates have important implications for public safety, the well-being of both those reentering our communities, as well as the communities that the formerly incarcerated individuals are being released back into. This study leverages a comprehensive prison admission/release dataset from the Washington State Department of Corrections in a spatial analysis looking at both individual level and county contextual variables with the intent to identify whether the county of release of a formerly incarcerated individual is correlated to the recidivism rates of the county. The analysis further considered social disorganization theory aspects as potential contributors to recidivism patterns. By incorporating these variables into a comprehensive exploratory data analysis, subsequent statistical analyses, and finally exploratory spatial data analysis methodologies the study aimed to understand how the socioeconomic context of the county of release may lead to a propensity to recidivate in Washington State. The findings of this project show that there is no evidence of a correlation between the county of release and propensity to recidivate in the State from 2012-2022, the conclusion drawn here is a finding for the null hypothesis of this study, that while many counties display statistically significant deviations from the sample mean, these deviations are not attributable to the county of release itself. Furthermore, contrary to academic literature that has found significant correlations between social disorganization and crime, no statistically significant correlations with the socioeconomic contextual variables meant to reflect social disorganization were found in this analysis. This suggests that while rates of public assistance enrollment and literacy rates or educational attainment have been found to be correlated to crime and recidivism elsewhere, they are not in Washington State at the county scale of analysis. The ultimate conclusion underscores a critical concern regarding the selected scale of analysis, emphasizing that the x County, as an areal unit of aggregation, proves to be too broad to comprehensively capture the nuances of recidivism. This assertion gains robust support from the evidence revealed by the analysis' findings.

To address the current climate crisis, environmental scientists and resource managers need to understand climate change impacts to classify appropriate conservation priorities. Current conservation efforts must focus on our changing climate to ensure the survival of vulnerable keystone species and ecosystems. The Joshua tree, a member of the agave family, is of vital importance to the Mojave and Sonora ecosystems. These cacti are classified as a keystone species as many desert mammals, reptiles, and birds rely on these trees for food and shelter. The clustering of Joshua trees within the southwestern United States is defined in this project as the Joshua tree forest (JTF). Previous climate studies have verified that the JTF, located in southwestern California, is critically threatened under business-as-usual climate scenarios. Consequently, the future vulnerability of the Californian JTF must be examined to preserve this unique ecosystem that thrives nowhere else in the world. Climate refugia, as used in this project, are locations that could be a haven for current species. Through classified refugia, areas, where species may migrate due to climate change, were identified to support state conservation priorities. This project created a suitability model using weighted overlays of climate, environmental, and land use variables to identify a suitable range of JTF refugia. This research ultimately classified 704,160 square meters of suitable JTF refugia based on projected climate data (2041-2060). Suitable areas were then compared to the current Joshua tree distribution providing insight into future areas where species populations are stable and where species can migrate as climate changes.

Russia’s unprovoked attack on Ukraine on February 24, 2022, sparked the largest armed conflict in Europe since World War II. As war in Ukraine continues, widespread reports of violations of human rights and international humanitarian law accompany extensive civilian casualties. Satellite imagery has provided unprecedented awareness of Russia’s war to corroborate testimonial evidence of human rights violations. While the use of satellite imagery is now commonplace to aid such efforts, human rights groups need improved remote sensing methods in active war zones. The objective of this study is to evaluate the suitability of freely accessible medium-resolution synthetic aperture radar (SAR) imagery from the European Space Agency’s (ESA) Sentinel-1 satellite versus expensive very high-resolution (VHR) optical imagery for the purpose of detecting war-induced building damage. The study area is the Ukrainian city of Mariupol, which was seized by Russia in May 2022. The study assesses building damage using backscatter intensity changes between images over time. Detected damage in conjunction with reports of civilian casualties may indicate potential violations of international humanitarian law. This study’s results indicate cumulative building damage in both extent and magnitude comparable to a United Nations damage assessment that relied on VHR optical imagery. Statistics estimate 27% damage from February 2022 to May 2022, which is lower than the 32% damage estimate by the UN for the same study area. While SAR imagery may provide less accurate results compared to VHR optical imagery, the increased timeliness, accessibility, and adaptability it offers may render SAR imagery analysis as a more feasible option for some human rights practitioners.

Approximately 11 miles of concrete channelized Los Angeles River (LAR) are planned to be restored to natural riverbed with engineered banks by the U.S. Army Corps of Engineers (USACE) working with the City of Los Angeles. Recent studies centered on urban watersheds in Southern California focus primarily on pollutants without addressing the effects of artificial riverbed substrate on water quality (WQ). Departing from the field collection methodologies employed by previous studies in the LAR, this project is a systematic remote sensing (RS) based investigation of differences in WQ found between natural and concrete riverbed environments in the LAR. Two RS indices are applied to aerial imagery to assess relative WQ in the LAR: Normalized difference vegetation index (NDVI) and normalized difference turbidity index (NDTI). National Agriculture Imagery Program imagery is integrated with NDVI to measure chlorophyll-a concentration and NDTI to determine turbidity levels as parameters to gauge WQ. Results indicate that natural riverbed sections typically contain lower NDTI values than concrete sections, suggesting these environments abate turbidity. NDVI results detect statistically significant differences in chlorophyll-a concentration between water in concrete and natural sections that may be the product of several variables deserving further study. The RS methodology herein provides a framework for monitoring of WQ parameters in the LAR. Provision of a dense dataset of WQ parameters existing in concrete and natural riverbed zones fills a critical knowledge gap regarding the poorly studied effects of the artificial substrate. Examples of natural riverbed sections in the LAR with engineered banks harboring reduced turbidity, while providing adequate flood protection for decades, supports the argument that
expanded restoration is both environmentally advantageous and safe for surrounding communities.

In the summer of 2020, sustained violence against Black Americans by law enforcement erupted into nationwide protests following the callous murder of George Floyd. The cultural zeitgeist prompted a call to action, not only to rethink our policing, but also to examine larger systemic and institutionalized racism in our society. In urban planning circles, this discussion often begins with an examination of the role “redlining” maps created in the 1930s by the federal government, which controversially appraised lending risk with a racial lens, stigmatizing areas with Black residents, outlined in red, as risky for investment, and contributing to ensuing segregation. Through examination of the nation’s largest metropolis, New York, this thesis evaluates whether redlining was only one factor of government policy – federal or municipal – entrenching segregation in the landscape. Global and local spatial clustering and segregation measures were conducted in 10-year intervals from 1910 to 2020 to evaluate underlying shifts in the spatial patterns of Black and White population segments over time. Linear regression, spatial error, and spatial lag models were then constructed to evaluate the degree to which redlining, urban renewal designations, public housing concentrations, zoning designations and historic districting contributed to the spatial segregation of Black and White populations in three distinct years: 1960, 1990 and 2020. The findings showed each era of new urban planning policy contributed to persisting segregation. The findings also showed that oftentimes a new generation of policy would spatially reference a prior era, to the benefit or detriment of a particular population: Urban renewal designations mimicked redlined areas and disproportionately concentrated public housing into increasingly Black enclaves, while exclusionary zoning tools like single-family zoning, often mimicked the safest investment designations in redlining maps, prolonging the privilege of predominantly White communities.

Land subsidence is an ongoing problem in California’s San Joaquin Valley. Due to drought and over extraction of groundwater, land subsidence occurs at a rate of more than one foot per year. Since California enacted the Sustainable Groundwater Management Act in 2014, land subsidence has been labelled one of the six undesirable effects that causes degradation of groundwater aquifers. Spatially assessing and identifying issues pertinent to land subsidence tends to come after subsidence has already occurred. Modeling land subsidence has been attempted with some success but doing so has required complex hydrogeologic models that are computationally intensive and require large volumes of data to be collected for processing and input. This research incorporated simple, but key geological and engineering variables that are derived from the United States Geological Survey and the California Department of Water Resources. From these sources, a robust dataset was used to statistically explore spatial patterns and relationships among groundwater levels, amount of fine-grained sediment present in the aquifer, confined or unconfined aquifer designation, well completion length, aquitard clay thickness, and well depth all as they pertain to land subsidence. Land subsidence patterns were assessed with exploratory techniques of generalized linear regression and geographically weighted regression. Each method was used to visualize the spatial distribution and scale of land subsidence relationships among groundwater wells from 2015 to 2021. Due to the size of the valley, the number of wells found throughout, and accompanying variability in independent variables, global scale predictions of land subsidence were not as successful as local regression techniques. Geographically weighted regression took into consideration the variance among variables, accounted for spatial autocorrelation, and yielded an easy-to-update, but accurate prediction for spatial patterns of land subsidence in the San Joaquin Valley.

In the United States, emission-releasing cars reign as the leading form of transportation among citizens. Given the increasing effects of global climate change, it is critical that society finds alternative solutions to travel. The use of geodesign, combining data-driven spatial analysis with thoughtful design and community input, shows promise as an approach to better design transportation infrastructure in the US. This thesis applies a geodesign methodology to propose biking infrastructure improvements in Redwood City, CA. It first assesses existing bikeability as of 2022 using a spatial analysis in a GIS. It finds that Redwood City has moderate bikeability with potential for improvements that if implemented, will simultaneously help solve issues related to economic, social, and transport inequity. The project next selects three specific street segments in the city that could most benefit from improved biking infrastructure. It makes the selection through a combined analysis of these bikeability results, assessments by local stakeholder organizations of underserved areas, and community feedback gathered at a public workshop organized by the author that focused on biking in the city. These three selected street segments underwent a design process, resulting in models and renderings of what improved cycling infrastructure could look like in Redwood City. This thesis ultimately serves as an exemplar methodology that can be applied to other cities in the US to increase local bikeability and improve long-term sustainability in terms of social equity and the environment.

Many online applications exist that allow users to search for hiking trails, but none have yet included the new Great Redwood Trail project that spans from San Francisco to Humboldt Bay in California. This “rails to trails” project is repurposing 320 miles of historic railway tracks in northwest California into public hiking trails. The new trails exemplify a positive relationship between human-created infrastructure and nature. Rather than creating more industrial waste, the Great Redwood Trail Agency is revitalizing the railway tracks for the purpose of promoting environmentalism and activeness in nature. For this project a mobile web mapping application was developed to familiarize hikers and the public with these trails. The Great Redwood Trail application provides and interactive digital map of the trails and includes relevant information and points of interest in surrounding areas aimed at stimulating the local economy, which is vital in garnering community support for trail development. Lastly, the app includes real-time wildfire information since this is now an incessant natural hazard in the Northern California region. Ultimately, this project aspires to showcase repurposed railways to hiking trails, promote local communities, and help ensure safety of app users.

Landslide susceptibility mapping incorporates variables such as slope, precipitation, and lithology, among others, alongside a wide range of different methodologies in order to generate maps that may aid in landslide prediction. Criteria in the literature is expansive and varied, and the weighting methods used equally so. Weighted overlay and fuzzy overlay were chosen and compared using a select number of criteria as a means of testing which method would yield a better, more accurate result. Between the two, fuzzy overlay appears to be the more accurate of the two methods after evaluating the outputs, and this is due to the ways in which the two methods classify criteria. Of the eight criteria used, slope has been the most influential criterion for both methods with lithology coming in as a surprisingly strong factor for the weighted overlay and drainage systems as a strong influence for the fuzzy overlay. This influence is reflected in the locations of areas of higher landslide susceptibility and reveal that weighting and bias have definite effects on the outputs. There then exists a circular influence between the outputs shaping decisions that may affect large numbers of people and decisionmakers’ opinions affecting criteria emphasis. Of the two methods used, fuzzy overlay produced less biased results than weighted overlay, as the emphasis used in weighted overlay are highly subjective and influenced by the user.

The Salton Sea is the largest body of water in the State of California and has experienced a decline in water quality within the last fifty years. This inland body of water serves as a reservoir for agricultural runoff and maintains high concentrations of pesticides and nutrients that place surrounding communities and ecological environments at risk. As a result of the degradation and eutrophic state of the Salton Sea, it is important to identify historical trends and methodologies that can be used for future water quality assessments. Traditional water quality assessments are conducted onsite and require extensive financial and human resources. In order to mitigate some of these costs while continuing to monitor water quality, more efficient assessment techniques must be explored. This study explores one such technique by examining the use of remote sensing techniques and the Normalized Difference Chlorophyll Index (NDCI) to assess chlorophyll-a concentrations in the Salton Sea from 2002 to 2020 using Landsat 5 TM and Landsat 8 OLI imagery. To assess the accuracy of this method, the NDCI is compared against two-band and three-band algorithms proposed by literature. Results indicate that the NDCI has largely underestimate chlorophyll-a concentrations within the Salton Sea and has incorrectly suggested small variations across the temporal range. Linear regression results further reveal a weak linear regression between NDCI, 2BDA and 3BDA values and in-situ measurements.

Critical topsoil is eroding at an alarming rate due to climate change and abrasive farming practices, with the United Nations predicting a catastrophic loss within the next 60 years. Losing nutritious topsoil, also known as soil (or land) degradation, will exasperate climate change and threaten global food security for a growing population that is expected to number at 9.7 billion by the year 2050. The greatest contributor to soil degradation is soil erosion, which is responsible for about 84% of the global extent of degraded land. Within the United States, soil erosion is heavily overlooked in the agricultural sector of Central Valley of California (CA), which is the nation’s largest food producing and exporting state. Despite its’ importance, the Central Valley has not been seriously evaluated for soil erosion, even though it has been intensely cultivated for agriculture production for more than 70 years. This project’s aim is to understand how differing land management practices in agriculture, combined with climate change factors, can alter processes of soil erosion severity in an agricultural area. Evaluating the county of San Joaquin, CA, future estimates of soil erosion by water are investigating using the Revised Universal Soil Loss Equation (RUSLE) in R and ArcGIS Pro (v.2.8). RUSLE was calculated for the year 2021 for a present-day point of reference and future predictions were calculated for years 2030, 2050, 2070 and 2100. For each year, the RUSLE equation was calculated using three different types of support practices, including: strip cropping, contour cropping and terrace cropping. Results show that when including future precipitation patterns, the practice of strip cropping generates the most severe soil erosion for each study year, with terrace cropping generating the least. Overall, the findings demonstrate that if farmers continue to employ strip cropping as opposed to other conservation-based cropping practices, they will lose necessary nutritious topsoil in just one to two generations.

Bird mortality from electrocutions and interactions with utility transmission infrastructure totals into the hundreds of millions globally each year. Birds with large bodies and wingspans are especially susceptible, because they more easily span energized and grounded lines and pole hardware. Avian electrocutions compromise transmission delivery and occasionally cause wildfires; therefore, utility companies are pressured to study and prevent them. Studies designed to evaluate contributing factors to electrocution typically examine pole design and appliances, but fewer studies investigate environmental and physical factors like slope, topography, aspect, vegetation, and proximity to water. Yet these factors can influence bird species presence and behaviors that contribute to electrocution risk. This study examines the Southern California Edison bird mortality dataset (1988 to 2012) used in recent research from California, which considers pole design and the presence of unpaved roads in non-forest areas. The results have predicted risk well for most species, but poorly for Golden Eagles, Turkey Vultures, and Common Ravens. The electrocution dataset was re-examined using road density, human population density, proximity to water, topographic variation, and dominant vegetation. Exploratory data analysis visualized avian electrocution patterns. Clustering occurred. Relationships between dependent variables (electrocution events) and the explanatory variables were modeled using logistic regression. Golden Eagle electrocutions occur in areas with few roads and poles with multiple conductors and are on level to moderately rugged terrain with low-growing vegetation. Common Raven electrocutions occur on poles where jumpers outnumber conductors in areas of higher road and population density. Turkey Vulture electrocutions occur in flat to intermediately rugged lands with tall scrub, woodlands, and grassland/woodland
mosaics.

Hong Kong is a dynamic city located on the southern coast of mainland China. A once unassuming island of fishing villages became an economical trading hub as a British crown colony following the Opium Wars. Hong Kong’s geology limited the natural area of developable land, and as the population of the colony increased over the decades, land reclamation projects were commissioned to account for exponential emigration. Over the course of its century-long history, the urban topography of Hong Kong has transformed significantly, and is expected to continue to evolve as the city maintains its status as an international business hub. This thesis explores the development of an open Historical Geographic Information System (HGIS) web application that portrays interactive digital versions of historic land use maps of Hong Kong. Historic maps of Tertiary Planning Units in Hong Kong’s Central district from the late 1960s to the late 1980s are the maps used in this application’s first iteration. This project incorporates georeferencing and spatial data creation techniques and methodologies for digitizing historical data and configuring app building software. The Hong Kong Historic Urban Land Use Web App successfully delivers the historic data in a user-friendly web-based application that allows users to investigate the land use differences and download the complete dataset for their individual purposes. The project also serves as a model for the development of similar web-based applications for exploring historical spatial data.

Most research literature on aggregate travel behavior and the built environment indicates that a dense, mixed-use, and transit-friendly settlement pattern generates lower automobile miles travelled than a traditional suburban development. By the same comparison, a substantial portion of research shows that any shift away from this ideal neo-urbanist community to more general urbanized areas exhibits only marginal – if any – influence upon travel behavior. Additionally, the commuter who must traverse such complex urban landscapes lacks information about the daily end-to-end costs associated with each practical mode of travel. This project’s GIS service package models the costs of driving versus transit, in minutes and dollars, for individual commutes from the perspective of a traveler. To sufficiently provide these spatial results, a network dataset was constructed for each travel mode – driving and multimodal transit – in the central metropolitan area of Washington D.C. The applied variables for driving included the cost of fuel per mile, travel time, parking time, and average parking fee. For bus, rail and pedestrian modes, the variables include average transit fares, walking, waiting and in-vehicle times, as well as these same inputs applied to any transfers. Commute times are summarized for each mode alongside corresponding dollar totals. For this cost conversion, annual income is extracted from location-based probabilistic income in traveler demographic data provided by StreetLight Data, Inc. Through web services development this thesis investigates a new approach for web GIS to model travel-cost information for individual commutes. These interactive services facilitate several use cases for research and transportation management, particularly if applied to invoke a commuter’s quantitative and qualitative response to mode choice. Where uncertainty currently prevails in modeling travel behavior, such empirical mode-choice data volumes become quite valuable.

Mountain search and rescue (SAR) incidents are high risk events that consume time and money, often placing the lives of rescuers and subjects alike in precarious situations. The increasing accessibility of satellite communication (sat-comm) devices for outdoor recreation may change when and where mountain rescue teams are tasked, and California’s SAR agencies need to understand the implications of emerging sat-comm device usage on SAR requirements to mitigate future risks caused by resource and training shortfalls. To date, no academic studies have conducted a holistic assessment of SAR incidents in the Sierra Nevada mountains or considered the impacts of sat-comm device usage on the SAR caseload. Such a knowledge gap impairs the ability of federal, state, and local agencies to anticipate costs and adequately train rescue teams to respond to mountain SAR incidents. This research explores the spatial and temporal patterns of historical mountain SAR incidents in the Sierra Nevada wilderness areas to understand how sat-comm devices impact SAR services in one of the most visited mountain regions in the continental United States. The results of this study suggest sat-comm devices are replacing traditional methods of notification that alert authorities to an emergency. Incidents where the subject communicates using a sat-comm device occur at sites of historical SAR activity where traditional methods of communication are dominant, as well as at new – and more isolated – locations. A lack of confidence in data quality, however, means this study primarily serves to demonstrate spatial and spatiotemporal analysis methods that SAR agencies may adopt to explore historical mountain SAR incidents at a regional scale.

About 99% of beef production in the United States occurs in concentrated animal feeding operations (CAFOs). CAFOs fatten huge quantities of cows in very dense, contaminated conditions before the cows go to slaughter. CAFOs use innovative technology like hormones, pharmaceuticals, and advanced feed to prepare cows for slaughter as fast as possible to maximize profits. These procedures come with negative impacts, like the emission of multiple airborne pollutants and the poor treatment of cows. This thesis focuses on the spatial distribution of CAFOs and the airborne contaminants they release. Prior research on airborne contaminant
dispersion from CAFOs has found that air quality is poor for communities where CAFOs exist. This thesis identifies 221 CAFO facilities in Texas and uses meteorological variables with a Gaussian plume distribution model to estimate the 2016 PM 2.5 concentrations released from each CAFO. Linear regression is employed to compare the results to measured PM 2.5 concentrations. The Gaussian plume equation is found to estimate PM 2.5 from CAFOs accurately. Close to the beef production facilities, the estimated concentrations reach up to about 759 μg/m3, which is higher than the national annual average standard range of 12 - 15 μg/m3. Finally, this thesis makes recommendations for a healthier beef industry.

Communities in the Houston-Galveston area of Texas are consistently at risk of hurricane devastation. With warming climates and increasing greenhouse gases, sea-level rise (SLR) has become a significant consideration. Many studies have shown the correlation between SLR and vulnerability, however, little has been found on the implications of SLR with the influence of storm surge on the community. This study established the current population and projected future population at risk in 2050 and 2100 from SLR and storm surge inundation in Houston and Galveston County. The National Oceanic and Atmospheric Administration’s (NOAA) projections of SLR of two-, three-, four-, and five-feet are combined with NOAA’s Sea, Lake, and Overland Surges (SLOSH) predictions to produce water surface elevations as sea level rises. A social vulnerability index was created, and weights were determined, using an analytic hierarchical process to reveal the socioeconomic vulnerable population within each water surface elevation produced. A cadastral-based expert dasymetric system method was employed to improve upon census data alone for spatial data of the population at 2020. An exponential smoothing algorithm was then used to predict future populations utilizing census data from Brown University and the American Community Survey from 1960 through 2020. The final assessment establishes inhabitants who were at risk in 2020 and the projected population in 2050 and 2100 within rising sea-levels. The results identifies the neighborhoods within Harris and Galveston County that are vulnerable to sea-level rise and storm surge inundation currently and in the future. This provides these two counties, and other government agencies, a geospatial assessment of vulnerable demographics within their locality and future estimates to assist in planning, preparation, and emergency response.

The fire season has lengthened as heat waves induced by global warming have created life treating conditions around the globe. One of the most affected regions is the West coast of the United States where, in particular, California experiences record breaking fires year after year. This trend is most likely to worsen in the following decades. In order to improve pre-fire detection, the remote sensing platforms use a combination of the integrated small satellites constellation and regular satellite platforms to provide an early warning system. The combined satellite early warning system relies on multispectral and multiresolution satellite networks. This statement of research proposes a fire susceptibility scenario that will attempt to delineate fire susceptible areas from (1) drought indices, (2) prediction, and (3) weighted overlay analysis. In order to avoid reliance 5+ hours latency between data transfer and data processing for state-of- the-art satellites. The proposed methodology of this study is to assess data pipeline from acquisition with a focus on short-interval pre-fire assessment that will delineate potential high-risk areas hence allowing officials to focus preventive measures accordingly. The research aims to improve the short-interval pre-fire data analysis by assessing the Bobcat fire outbreaks and taking a closer look at pre-fire detection methodology. Results from weighted overlay analysis scenarios delineate areas that are classified as susceptible. On the other hand, prediction and drought indices scenarios do not yield expected results.

Hurricane Ida struck southeastern Louisiana with winds greater than Hurricane Katrina, and truly tested the rebuilt levee systems of New Orleans and lower Louisiana. Weather and governmental agencies used predictive models to anticipate and predict storm surge locations and severity, as storm surge from hurricanes is the leading fatality cause globally during tropical events. Current tropical systems are fueled by climate change that is impacting storm strength and regularity, yet storm surge models must ensure the highest degree of accuracy. The National Oceanic and Atmospheric Administration’s Sea, Lake, and Overland Surges from Hurricanes (SLOSH) and the Climate Resilience Center 's Advanced Circulation (ADCIRC) models are two preeminent models that simulate and predict storm surge in an effort to publish evacuation orders and save lives. Hurricane Ida rapidly intensified prior to landfall, which stressed the ability of agencies to properly predict storm impacts. In this project, SLOSH and ADCIRC were tested and evaluated against each other, and against observed flooding during Hurricane Ida to determine model strengths and weaknesses. Results show that both SLOSH and ADCIRC overestimated storm surge extent, but underestimated surge depth. ADCIRC was more accurate in long range
forecasting, while SLOSH was more accurate in short range forecasting. Recommendations are posed to enhance the accuracy of current storm surge models. This spatiotemporal analysis can help validate surge models against climate enhanced storms with the goal of saving as many lives as possible.

Geographic information systems (GIS) contain visualization and analytical tools that assist users to better understand the spatial and temporal relationships between mapped entities. The formation of music genres, for example, is a complex phenomenon that can be explored through spatial analysis using GIS software. The genre of punk music emerged in New York City, NY in the early 1970s with the appearance of bands such as the New York Dolls and The Ramones. Punk music is unique because commercial or mainstream success is most likely not the sole motivator for musicians who propagate the genre. The Do-It-Yourself (DIY) mentality of those who originally played punk music, coupled with the unique subcultures that stemmed from local groupings of popular bands raises questions as to the nature of the environment and people closest in proximity to the phenomenon. This study aimed to explore the spatial and temporal relationships between genre-specific punk bands and their local environments within the context of sociodemographics and time. A literature review was conducted to identify the temporal and spatial evolution of punk music. ArcMap and ArcGIS Pro were used to analyze, display, and prepare the spatial data, which included locations of band formation sites, venue openings, and census data from 1970. Point cluster and proximity analysis, along with historic census data quantification were employed to tell the story of punk music within the context of time and space. The people, environment, and spatial diffusion of locations (as well as attributes) associated with early punk music is characterized through the use of GIS. Findings revealed through this research exemplified the versatility of GIS and created a repeatable process for examining other music genres.

This thesis focuses on prehistoric hunting patterns for targeting desert bighorn sheep within the archaeological complex located at Little Lake in Inyo County, California. The study area is situated on the eastern margin of the Sierra Nevada and on the western edge of the Coso Range. Little Lake has long been of interest to archaeologists due to the density of rock art and prehistoric archaeological sites surrounding the lake. This study uses a geographic information systems (GIS)-based analysis to investigate the locational properties of five prehistoric stone features and to analyze how they may have been employed as hunting blinds to pursue the desert bighorn sheep (Ovis canadensis). Using elevation models and map algebra functions in ArcGIS Pro, I modeled the behavioral characteristics of bighorn sheep and performed a visibility analysis aimed at interpreting past hunting strategies. A 10-meter digital elevation model (DEM) was employed to visualize bighorn sheep escape terrain and produce macro-viewsheds, while an unmanned aerial vehicle (UAV)-derived three-dimensional (3D) model of the study area was used to generate micro-viewsheds using 3D visibility tools. Results indicate a relationship between hunting blind locations and escape terrain for desert bighorn sheep, and the visibility analysis at the local and landscape scale allows for the reconstruction of prehistoric hunting practices at Little Lake.

Global climate change is proceeding at an unprecedented rate, and one species that is particularly vulnerable are green sea turtles. Green sea turtles are excellent indicators of climate change impacts on coastal and marine habitats as they rely on both at different points in their life cycles. The green sea turtles (Chelonia mydas) were added to the Endangered Species Act in 1978. In 2015, a status review completed on the now eleven distinct population segments (DPS) identified three DPS as endangered and the other eight as threatened. Out of these eleven populations, this paper assesses the extinction risk of the endangered Central West Pacific (CWP) DPS with Maxent habitat suitability modeling of nesting sites under current climate conditions and an extensive assessment of factors influencing population dynamics. The Maxent model used 101 green sea turtle nesting sites located within the CWP DPS and seven of the 19 bioclimatic variables from WorldClim clipped to within a 12 kilometers shoreline buffer, because green sea turtles only nest along the shorelines. The Maxent results calculated the suitability threshold for the CWP DPS was 0.0652, which means that values below that threshold are nesting sites that are considered not suitable, and values above that threshold are nesting sites that are considered suitable. Out of all the shorelines in the CWP DPS, only 26 percent were considered suitable nesting habitat. The extinction risk analysis followed a criteria written for this thesis based on the knowledge of extinction risk status assessments of the IUCN Red List and Seminoff’s 2015 Status Review. The results of the extinction risk analysis of the CWP DPS indicate they are at a medium risk of extinction. Although this population is not indicating a high risk of extinction currently, their population abundance is still low enough to be considered endangered which warrants more effective and efficient conservation measures to be implemented.

Identified by polycultures of plants and animals, regenerative farms are made up of complex interrelated systems and face challenges with data management and record keeping. Despite regenerative farms having more complex record keeping needs than industrial (monoculture) farms, they are not well supported by existing farm management software. A spatial database can be a powerful tool for organizing, accessing, and analyzing farm data. The objectives of this research are to design and create a functional demonstration of a spatial database for agricultural record keeping that is tailored to the needs of regenerative farmers. The initial database design was informed by an extensive literature review of record keeping technologies in agriculture as well as the author’s professional experience working on regenerative farms. The database’s logical schema was finalized after conducting interviews with farmers and leaders of the regenerative movement in Ventura County, CA. Nine interview subjects representing five regenerative agriculture organizations participated in this study. The farms had varying record keeping practices, from memory, to spreadsheets, to Farm Management Information Systems (FMIS). A spatial database was created in PostgreSQL with the PostGIS extension and populated with archival farm data to demonstrate the database’s usefulness to regenerative farmers. The data was combined and visualized through SQL queries that leveraged the relational, temporal, and spatial qualities of the farm data. While this spatial database requires technical proficiency to set up and maintain, it was found to be more effective at handling a farms’ data than their current record keeping systems. Spatial databases are well equipped to handle the data needs of a regenerative farm.

This work endeavors to elucidate the parallels in technical methodology between the geospatial and video game development communities in order to understand how these methods can be harnessed by technologists within the domain of GIST. By understanding the core technical principles, software, and use cases that serve to mitigate and manage the core challenges shared between GISci technologists and game developers, this study aims to progress the standard technical workflow within GIST in learning from this sphere. An in-depth literature review surrounding the relationship between the two fields and the strategies used to conquer issues ranging from spatial data acquisition to graphics processing challenges followed by a qualitative investigation comprised of 16 interviews with subject matter experts (SME)s in both the GIST and gaming arenas was conducted in arriving at this study’s findings. Core elements of human computer interaction (HCI) techniques from navigation/wayfinding strategies to scaled representational methodologies such as and multidimensional depictions of the environment were central to the investigation of shared methodological approaches examined between these two fields to appreciate the manner in which game development methods can be applied within GIST.
The major types of analyses used within this study of these two domains were based on an in-depth literature review on this intersection together with subject matter expert interviews from those in the GISci and gaming arenas. By uncovering the manner in which the gaming community can pass on technical knowledge onto GIST, this study hopes to illuminate a greater understanding of the way in which both domains can learn and collaborate with one another to create a shared community body of knowledge and grow from this methodological exchange.

Recent severe flooding caused by storms, such as Hurricane Sandy in 2012, has damaged vulnerable coastal communities across the United States at an increasing occurrence and severity. Not only do floods threaten lives and property, but they also alter the shape of a community through imbalanced recovery among socially and economically vulnerable populations. This concern begs the research question: what, if any, are the differences in recovery between communities of different economic standing concerning flood inundation levels after a severe coastal flooding event? Economic recovery disparity was investigated by analyzing New Jersey's socio-economic structure before and after Hurricane Sandy according to inundation depths categorized as impact zones: None (NIZ), Minor (MIZ), Serious (SrIZ), and Severe (SvIZ). The research design was developed to (1) examine the physical exposure of Hurricane Sandy across New Jersey; (2) investigate the socio-economic characteristics of New Jersey communities before and after Hurricane Sandy; and (3) determine whether, or not, proximity to severe flooding resulted in notable changes to citizen’s economic standing. The analysis compared tabular data from 2010 and 2018 American Community Survey (ACS) 5-Year Estimates using three evaluations: population, income, and housing. Results displayed variable levels of impact throughout the entire study area from 2010 to 2018 regarding population, income, and housing; however, results did not show statistically significant relationships between economic recovery and flood inundation levels.

With the rising threat of climate change, the State of California committed itself to have all vehicles sold within its borders to have zero emissions by 2035. The State dubbed this strategic plan “Transportation Electrification,” which includes Senate Bill No. 100 California Renewables Portfolio Standard Program and Executive Order (EO.) B-48-18. California is in need to implement proper infrastructure to accommodate the influx of electric vehicles (EVs) on its roads to accomplish this goal. This project uses geospatial analysis approaches to determine the readiness of the Los Angeles County region in support of a 100% EV-owning driving population. Criteria for identifying a location’s readiness were based on the California Governor’s Office of Business and Economic Development’s (GO-Biz’s) and the US Department of Health and Human Services’ (HHS’s) public programs, and related studies. This project used the following criteria when evaluating locations’ support of EV drivers: (1) population distribution, (2) traffic, (3) proximity to other charging stations, and (4) governing body. By conducting a geospatial analysis (i.e., “summarize within” and “hotspot analysis” ) , the result indicated that most Los Angeles County areas, especially cities/communities in the Santa Clarita and Antelope Valleys, lack sufficient charging stations to support the State’s vehicle electrification goals. Particularly underserved populations are the County’s unincorporated areas (e.g., Malibu Bowl, Monte Nido) and areas with a high population density (i.e. the Cities of Maywood, Huntington Park, and Cudahy). Ultimately, this project identified locations’ readiness for an EV driving population, which will lead to proper EV infrastructure development that reach the State’s carbon emission goal and granting easier EV-charging access to Los Angeles County
residents and visitors.

Pharmacies are one of the most used healthcare facilities across the nation due to of the number of services provided within a retail setting. The services provided by a retail pharmacy include dispensing of medications, vaccines, diagnostic testing, counseling, and more, all of which are pertinent to creating and maintaining a healthy community. A pharmacy shortage area is a location or area with an inadequate number of pharmacies available to a community leading to residents traveling far distances to their nearest pharmacy. Determining states or parts of the country that have a large number of these shortage areas provides a blueprint of where pharmacies are greatly needed. This project provides a national distribution of the accessibility of pharmacy locations by census tract for 2018 and 2020. These results are presented in a web mapping application through ArcGIS Pro. By presenting pharmacy shortage areas by census tract and over time, policymakers have highly relevant information. Given this information, there is a better chance for federal, state, and local policy changes that may improve pharmacy accessibility overall.

In 2018, Houston’s crime rate was higher than the rates in 95% of U.S. cities. Houston’s population is the fourth highest in the nation with more than 2 million people, all of whom are affected by this high crime rate. A better understanding of the spatial and temporal aspects of crime would be useful for law enforcement in protecting the general population. This study analyzed assaults, burglaries, robberies, and thefts in the inner Interstate 610 area of Houston, which is considered downtown. The Houston police department provided crime address data for each crime type from 2018 to 2020. The crime data was geocoded in ArcGIS Pro into point shapefiles and aggregated using counts. The Esri Optimized Hot Spot Analysis and Kernel Density Tools were used to determine crime hot spots for each crime type. The study also explored whether land use type was related to hotspots of certain crimes in the Downtown and Midtown districts of Houston. The study found that the crime hotspots for each crime type occurred mainly in the Downtown, Midtown, and Montrose districts of Houston. Thefts and assaults were higher near the downtown bar district. Theft was also higher near bus stations. The study results could be valuable in helping the police predict and respond to crime hot spots in the future in the Houston area, and the methods used may help the police manage crime in other geographic areas and over different time periods as well.

Large and severe wildfires have become the norm in many parts of the western US, including the region along the Oregon-California border. As populations in this area continue to grow, they encroach on undeveloped land with abundant wildland fuels and high fire risk. Communities that inhabit this wildland-urban interface (WUI) are increasingly imperiled as climate change exacerbates catastrophic fire activity. While previous, national-level studies have established a methodological baseline for WUI identification using vegetation and population density data, the impacts of variable criteria on small-scale study areas remains under investigated. This is a key area of concern because the adequate identification of WUI communities is a vital first step for effective public policy decision making, emergency planning, and resource allocation. This project attempts to bridge the current research gap by analyzing the impact of vegetation and population variable parameters on the size and character of identified WUI areas for ten counties along the Oregon-California border. This analysis is used to generate an optimal WUI definition for the project area, which defines the WUI as census block groups with ≥1 household/400 acres and ≥25 % wildland vegetation cover. This project finds that, compared with previous national-level studies, a much lower population density threshold is necessary to adequately identify plausible WUI communities. This study also supports previous findings, which indicate that vegetation density thresholds are of secondary importance when compared to population density. These findings are of interest to land managers who are tasked with resource allocation and wildland firefighting in the WUI, along with residents who inhabit these communities.

Land managers and ecologists aim to maintain the healthy balance of an ecosystem. Ecosystems are not static but are vulnerable to change and have been especially impacted by humans. Ecological restoration often involves reestablishing habitat to a previous condition or mitigating changes in ecosystem functioning. Stewards of the land must understand an area’s historical ecological context to inform restoration decisions. In Marin County, the study area for this thesis, woody plant encroachment caused by fire suppression is an ecological concern. Where indigenous people once managed the land with frequent burning, fire suppression throughout the past two centuries has caused ecological changes. Transitions from grassland to shrubland and from shrubland to woodland are a result of woody plant encroachment and can lead to decreased biodiversity. This thesis classified and compared historical and modern aerial imagery to assess changing vegetation communities in Marin County. Land cover change was calculated and visualized from 1952 to today. Ultimately, it was found that herbaceous plant communities and shrubland have shrunk by 62% and 51%, respectively, while woodland has increased by 307%. The mosaiced landscape of 1952 is now more homogenous. 44% of total woody plant encroachment consisted of woodland replacing shrubland, while 39% consisted of woodland replacing grassland, and 17% consisted of shrubland replacing grassland. More shrubland was lost than gained, and the most common shrub species replacing grassland was coyote brush. The most common woodland species replacing grassland and shrubland was Douglas fir. These results point to specific targeting of coyote brush and Douglas fir establishment in areas of known encroachment. While this study provides valuable data on type conversion over the past 70 years, future research should focus particularly on vegetation changes in the last decade to support proactive approaches to managing encroachment.

Geologists have studied Earth’s long-runout landslides for many decades due to their unpredictability and massiveness. Long-runout landslides on Earth largely depend on initial mass position, friction, slope, topographic relief, gravity, and sediment composition. Landslides on Mars exhibit a high degree of preservation, offering insights into the planet’s history, including the occurrence of water in past eras. This study examined long-runout landslides in the Valles Marineris canyon system of Mars and compared them to similar landslides on Earth. The methodology developed in this study utilized GIS tools and High-Resolution Stereo Camera (HRSC) imagery to visually interpret and measure the mass movement of long-runout landslides of affected terrain. Landslide visualizations and cross-sections were manually created to facilitate estimating changes in the length and height of the Mars long-runout landslides. These measurements were used to calculate Heim’s ratio, an approximation of the friction coefficient of Mars surface regolith comprising the landslide masses, and to compare these values to those of similar long-runout landslides on Earth. The goal was to test the assumption that friction, specifically, plays a significant role in long-runout landslides on Mars. In the future, an improved understanding of long-runout landslides on Mars may assist scientific communities in interpreting Mars’s geological processes and climatology and illustrate important knowledge gaps in Mars history.

Just over 14% of the Canadian Arctic Ocean has been surveyed using multi-beam sonar mapping techniques, and comprehensive charting of navigable shallow water is even more scarce. With the use of LiDAR satellites and multispectral sensors, optically shallow water that is free of sea ice can be modeled remotely through a process called Satellite Derived Bathymetry (SDB). Using two empirical algorithms, the multiband value algorithm (MBVA) and the two-band value algorithm (TBVA), bottom depth in the Bellot Strait is interpolated. The study area is between 71˚ and 73˚ of latitude where North America meets the Somerset Island. Shallow water depth can be interpolated using LiDAR data which is collected by the ICESat-2 satellite, equipped with the Advanced Topographic Laser Altimeter System that records ellipsoidal heights with uncertainties up to 0.70 m. The multispectral data is provided by the Landsat-8 Operational Land Imager constellation at 30 m resolution. The MBVA and TBVA use refraction corrected LiDAR depths within raster cells which overlap LiDAR returns to create a training dataset to inform interpolation of the SDB depth. SDB offers the ability to chart remote shallow places that would be difficult and expensive to reach otherwise. With the introduction of the Seabed 2030 project by the International Hydrography Organization and the United Nations this past year, LiDAR refraction correction and SDB modeling has the potential to accelerate charting of coastlines, contributing towards our global pursuit to create stronger models of the Ocean seafloor and improve maritime safety. This thesis assesses the challenges of conducting SDB analysis in the Arctic region, using the Bellot Strait as a study area. Where appropriate remote sensing data exists, this process can be repeated.

Sites listed on the Environmental Protection Agency’s National Priority List (NPL) are some of the most polluted or contaminated locations in the United States. Only locations that have been evaluated as posing the greatest widespread and imminent threat to human health and/or the biophysical environment make it onto the NPL, and Santa Clara County (SCC) in California is home to twenty-three of them. Since the creation of the NPL and associated Superfund programin the 1980s, hundreds of studies in the field of environmental justice have provided evidencethat the burdens of environmental hazards, like Superfund sites, are not distributed equally across racial, ethnic, or economic groups. Thus, in an effort to better understand the extent of this idea this project seeks to ascertain if a spatial disparity in the distribution of Superfund site locations within SCC exists today and whether post-siting demographic change occurred around sites within the county. This project maps the locations of active and historic Superfund sites in addition to completing a longitudinal, area-weighted analysis of the surrounding communities and study area. By spatiotemporally assessing theories associated with hazardous waste sites and disparities, this project ultimately seeks to provide a clearer understanding of how environmental hazards and disparities can affect and shape the communities in which they are found.

Researchers in the field of Spatial Sciences often use Geographic Information Systems (GIS). Marketing, a sub-field of business, has increasingly used GIS to address and solve marketing problems. The Marshall School of Business at the University of Southern California (USC) has only recently recognized the importance of teaching GIS to better prepare their students for the workforce. The school sought to rectify the situation through a marketing course focused on the analysis, interpretation, and application of spatial data. However, when a transdisciplinary course is created, the disciplinary constructs must be informed by disciplinary experts and the design grounded in educational research. This thesis examined disciplinary thinking in Spatial Sciences and Marketing to inform the development of a graduate elective course “GIS and Decision Making in Marketing.” Interviews were conducted with USC professors in both fields on the nature of disciplinary thinking, approaches to research and analysis, and commonalities between fields. These interviews were analyzed using content analysis. In general, marketing is an evolving discipline that currently defines disciplinary thinking as gathering and analyzing data, applying frameworks, evaluating constraints and assumptions, and drawing actionable conclusions. Spatial sciences sees disciplinary thinking as knowing theories, using improved computational tools, and engaging in spatial thinking, reasoning, and communicating. These findings were incorporated into the development of a transdisciplinary curriculum for the elective, which fostered students creating knowledge. While it is too early for formal assessment, informal assessment of the unit suggests that it improved students’ ability to reason spatially within the marketing context. This project can inform other business schools seeking to integrate GIS into their curriculum, or other fields seeking to engage in transdisciplinary approaches to education.

Efforts to address the homelessness crisis in Los Angeles have made little progress in the last few years. As homelessness has increased, government officials have struggled to find solutions that will benefit both the homeless community and mainstream residents of Los Angeles. Furthermore, the city may not have enough resources and space to support the growing homeless population. Thus, this research examined the spatial correlation between the availability of resources and the homeless community. It also addressed the current accessibility of community resources and investigated whether Los Angeles is equipped to meet the needs of the growing homeless community. This research employed a service area analysis in ArcGIS Pro to determine the walking distance of food, hygiene, shelter, and transportation services via walking and identified key neighborhoods with large homeless population that are lacking in certain types of community resources. The results have shown that all four service types are available in neighborhoods with a large homeless population, such as Downtown and Historic South Central. However, food and hygiene services are lacking in some neighborhoods with a smaller but significant homeless population, more specifically, in the northern regions of Los Angeles. Shelter and transportation services are adequately available throughout Los Angeles. The limitations of this research were discussed in the last chapter of this thesis.

The Santa Clara County economy has fueled demand for commercial real estate (CRE)
developments. CRE brokers capitalize on this demand by attracting tenants to newly constructed
buildings or by helping property owners sell to developers for future redevelopment
opportunities. City planning departments disclose construction project information within their
boundaries. Each city has its own property methodology and data release schedule with few
sources recording these sources regionally. The main objective is to create a CRE construction
web application that tracks construction projects within the major cities of Santa Clara County.
This helps save time by standardizing the project data and aggregating all the information into
one program. The progress of the CRE development is tracked throughout the pipeline as it goes
through the following statuses: pending approval, approved, and under construction. The
database also focuses on the following property types: office, hotel, multifamily, retail, and
industrial. This construction data is organized and presented to brokers via a web GIS application
to enable spatial searches in an easy-to-use interface.

The Lincoln Littoral Cell (LLC) contains a 24 km stretch of coastline along Oregon’s central coast. Nearly 50% of the LLC’s coastline has been armored with shoreline protection structures (SPSs), mainly riprap and seawalls. SPSs are constructed to reduce damage to coastal developments caused by breaking waves, flooding, and sediment erosion. Although the SPSs are meant to protect the coast from erosion, they can ultimately cause erosion adjacent to the structure or further down shore. Future projections and models show an increase in frequency of large storm systems that generate larger than average waves and water levels, resulting in increased erosion and flooding. This project utilizes the USGS’s ArcGIS add-on, Digital Shoreline Analysis System (DSAS), to analyze digitized shoreline positions from 1997 to 2016. Visual analysis shows that while on average the shoreline is accreting at a rate of 0.32 m/yr, there is localized erosion adjacent to 53% of the SPSs. Future policies regarding the placement and build of SPSs should take into consideration the long-term negative effects of these structures.

One possible costly outcome of large earthquakes is breakages in the ground surface, known as
surface ruptures. Surface ruptures can cause damage to human infrastructure as well as harm
humans. Detailed field studies that trace these structures take substantial time and effort because
they affect large regions, but this is reducible by collecting remote sensed imagery performing
supervised classification on the imagery with a GIS. For this study, unmanned aerial vehicles
(UAV) imagery from the Ridgecrest area in California was analyzed using the support vector
machine (SVM) classification method to attempt surface rupture detection in desert terrain. The
imagery covers a small area, so a K-Folds analysis method was used to attain statistically
significant results. This involved splitting the imagery into 5 random pools of 30-meter by 30-
meter squares before running the classification. The results of each classification were analyzed
by generating confusion matrices and visual inspection. An average of the five confusion
matrices was used for a final analysis. While this method did classify larger segments (> 0.5-
meter-wide) of surface rupture that was in the image, it missed most of the smaller surface
rupture segments (< 0.5-meter-wide). In addition, the technique misclassified parts of the
imagery as surface rupture, especially around the vegetation, road paths, and amongst a rock
field in the south-east corner. Further testing should be done with this method, including using it
on imagery with different land-cover. Based on the results of the further testing it may be ready
to try in practice scenarios for real-life earthquake disasters.

Regulation of paleontological resources began with the creation of the Antiquities Act of 1906, but it was not until the passing of the Paleontological Resources Preservation Act (PRPA) of 2009 that the regulation was clarified on the following: permitting, penalties, inventorying, amateur collecting, curation, and education and research in certain departments within the Department of Interior (DOI) and the Department of Agriculture. This served to further protect paleontological resources within public lands through the use of scientific expertise for the purpose of education and research. ❧ Museum collections are held within public trust for the purpose of education and research and are curated to the highest scientific standards. These institutions work with federal institutions to preserve paleontological specimens found on public lands in accordance with the PRPA. Accessing collections protected by the PRPA requires written permission and location data is exempt from being revealed. Digitization of paleontological collections that are not protected by the PRPA has been a slow process. ❧ Online collaborative databases such as Integrated Digitized Biocollections (iDigBio) and the Global Biodiversity Facility (GBIF) offer museums the chance to combine with other museums and institutions to develop a global database that shares highly curated biological and paleontological collections for the purpose of education and research. This evolution of museum collections can benefit spatial research as biodiversity data standards become more developed. However, this evolution has also been slow, and research incorporating paleontological collections with GIS is limited. ❧ The objective of this thesis is to present a case study focusing on Petroleum County, Montana, where a dual geodatabase system was created incorporating both museum and field collections to assist with site management, assessment, and inventorying. A customized model toolbox allows for paleontologists to perform analyses related to their hypotheses. These geodatabases are designed for the sole purpose of simplifying and aiding in management practices through the incorporation of museum collections. Museum collections are already incorporated with these assessments and inventorying. With the addition of GIS, museum collections’ involvement will bring new possibilities for how their collections are applied.

People become lost and injured in remote areas on a daily basis. Search and rescue personnel,
including members of the military, law enforcement agencies, fire departments, emergency
medical services, and volunteer teams stand by for the opportunity to rescue someone. This
requires the development of teamwork and skills and the acquisition and maintenance of
equipment. Rapid and accurate analysis of geographic information plays a critical role in
ensuring resources are used as safely and effectively as possible in search and rescue operations.
Historical search and rescue records contain a wealth of information that can guide
training, drive recruitment of new members, and define the necessary capabilities of equipment
and personnel. Although they may not have been collected with the intent of providing data for
spatial analysis, the geographic information contained in rescue documentation can provide
information as to the trends in where rescues are occurring, which can help anticipate future
incidents and improve readiness.
This thesis is a retrospective application of geographic information science to the records
of the San Bernardino County sheriff’s air rescue program. The hoist rescues performed since the
program’s inception in 1993 were georeferenced, digitized, and used as the basis for a
geodatabase. The spatial pattern of air rescues in the county was analyzed to identify the areas of
greatest rescue activity and seasonal trends in locations and elevations. This information will
help leaders on the team guide training and preparation and help define the necessary capabilities
of rescue helicopters for work in the county. In addition, by showing trends in rescue activity in
certain areas and times of the year, recommendations are made for preventative measures to keep
hikers and climbers safe. The analysis methods here can be applied to similar projects throughout
the United States and beyond.

Short of becoming homeless, everyone must live somewhere, but the circumstances leading to an individual’s choice of housing can be complex. Housing choices represent both personal factors and outside influences and are often wrapped up in the overly simplified concept of “housing affordability.” In California, the unaffordability of housing is particularly acute. This thesis uniquely combined multiple datasets from the US Census Bureau and the US Department of Housing and Urban Development to classify areas of the state according to the number of select housing-related challenges that residents experienced as a result of their housing accommodations. The challenges were then mapped, individually and collectively, to observe the geographic distribution of the phenomena. This innovative method supplements the 30-percent ratio (of housing costs to income) methodology traditionally used to denote housing affordability and adds a visual and spatial display of housing challenges at a statewide level and in several focus areas that have been negatively impacted by the current housing crisis. Finally, a review is provided of existing and potential solutions to the four housing challenges investigated. The results may be of interest to affordable housing providers, legislators, and even residents.

The food industry has been completely disrupted over the past 5 years with the onset of platforms like Doordash, Instacart, HelloFresh, and Hungry Marketplace. These platforms, and others like it, offer customers timely, cost-saving, and convenient ways to prepare, consume, and/or experience meals. The explosion in food-industry innovation has changed the dynamic of food services altogether as status quo operations aren't meeting the needs of the innovative oper ations. More people are opting in to having their meals delivered to more comfortable, familiar, locations as opposed to going to brick-and-mortar restaurants to consume their meals. As a re sult, the food industry’s changing dynamic has led many to reconsider owning expensive restau rant locations in consideration of alternative commercial locations that are much cheaper and al low them to deliver the same quality of service. Given the importance of kitchen and office loca tions and the critically changing nature of the industry, new methods are needed to determine the optimal locations for companies that are delivering services in this new environment. Using a GIS (Geographic Information System), this thesis uses spatial analysis including site-suitability analysis and network analysis to build an optimization model for Hungry Marketplace, a food industry start-up. The model utilized the company’s current operations in Boston, Massachusetts as a case study. The model identified optimal locations for kitchen and warehouse operations that maximize the market opportunities while keeping the operational expenses low. This thesis pro vides recommendations to the company for a cost-effective operation going forward. Ultimately, this data-driven and reproducible methodology can be applied by existing and potential companies in the food industry for optimizing their spatial decisions.

Tracking volcanic ash clouds is integral to ensuring the safety of people traveling by air and living on the ground. The explosive eruption of Eyjafjallajökull volcano, Iceland, that began on April 14, 2010, produced a column of steam and ash carried by the jet stream to Europe. In London, the volcanic Ash Advisory Center provided the ash cloud's overall spatial extent across Europe but was unable to provide localized forecasts or details such as cloud density. This eruption forced the most prolonged suspension of air travel in Europe since the second world war with significant cost to airlines and travelers. ❧ High temporal, multispectral thermal satellite imagery from the MODIS instrument provides a platform for tracking eruptive materials. This study uses the brightness temperature difference between MODIS thermal bands to track the ash cloud for each day of the eruption. Unsupervised classification is used to determine the spatial extent of the ash cloud. Measurements from the ground-based EARLINET Lidar network are used to compare the results derived from MODIS imagery. Interpolations based on the EARLINET Lidar data are comparable to the MODIS classification but may not be economical for developing regions.

A vast array of systems, including cellular telephones and GPS receivers, use Radio
Frequency (RF) signals for communication. These devices transmit and receive RF signals, and
when one of them is transmitting, there is the potential for interfering with other nearby
receivers. The objective of this work was to build a tool to analyze potential interference between
cell telephone transmissions and high accuracy GPS receivers used in land survey equipment.
Many Geographic Information Systems (GIS) applications, remote sensing systems, and
specialized land survey receivers depend on highly accurate location services such as the Global
Positioning Satellite System (GPS) and cellular telephone networks. GPS location is ubiquitous
in GIS applications and allows land surveyors to accurately measure location, distance, and area.
Unfortunately, since cell signal frequencies overlap GPS frequency bands and can interfere with
GPS signals upon which land survey equipment relies.
This thesis documents the development of an innovative automated toolset called SurvInt,
which quantifies how spatially pervasive, severe, and variable the impact of cellphone signal RF
interference is in Los Angeles County. Los Angeles is the study area for SurvInt for three
reasons. First, the county includes 88 separate cities to test the SurvInt tools. Second, the
county’s variable terrain ranges from sea level to 3068m to tests critical visibility calculations in
SurvInt. Finally, LA County makes most SurvInt data publicly available.
SurvInt provides two direct benefits to land surveyors. First, SurvInt helps visualize the
spatial distribution of RF interference. Second, SurvInt identifies the locations and operators of
specific interfering cell towers so that interference mitigation efforts can be planned. Both
benefits exemplify the power of GIS to help manage complex interactions between spatially
distributed cellular telephone networks.

Students returning to university and college campuses amid a global pandemic, political unrest,
and a rapidly changing climate, are at an increased risk of mental health challenges like anxiety
and depression (Berry et al. 2018; Gao et al. 2020). During this period of turmoil and beyond,
individuals on and around school campuses will likely want and need spaces where they can
experience joy. Mental health and well-being are crucial facets of overall health. When these
issues are not addressed or treated, the ill effects can impact lives, proliferate into communities,
and negatively affect academic performance, retention, and graduation rates. Conversely,
research has shown that reflecting and sharing positive experiences can increase positive affect,
happiness, and life satisfaction (Lambert et al. 2013). The objective of this thesis is to develop a
community-centered Web GIS application that lets users map places where they experience joy
using an online cloud-based GIS platform to create an intuitive crowdsourcing interface. This
study focused on a region encompassing the University of Southern California’s University Park
campus (USC UPC) and used USC community members to beta test the application and
determine limitations and future improvements to the user interface, application workflow, and
functionality. The application enables students, staff, faculty, and other community members to
record their state of well-being and experiences of joy at different geographic locations and to
observe the perceptions of joy and well-being of others around campus. University planners and
university mental health professionals can utilize this data to make better decisions regarding
mental health programs, campus design, and student outreach and education. Beta testing and
feedback revealed that future work includes improving the database and storage, reaching out to
stakeholders to involve them in the design, and continuing to customize and develop Happy
Traveler with additional developers.

Each year, the Los Angeles Homeless Services Authority (LAHSA) conducts its Homeless
Count, enumerating people who are experiencing homelessness in Los Angeles County. The
count includes a Demographic Survey, where surveyors interview unsheltered people in a sample
of census tracts in LA County. The survey data is a key tool for informing homelessness policy.
The survey’s current sampling methodology does not account for the spatial relationship between
tracts but approaches the distribution of homelessness in a tabular way, using a “hot-spot
planning process” that relies on administrative boundaries. LAHSA also uses administrative
boundaries to sample tracts rather than accounting for the characteristics of the tracts where
unsheltered people tend to live. This represents an opportunity for a spatial analysis approach to
homelessness data that improves the stability of results, accounts for spatial variability in the
data, and characterizes areas in ways that are relevant to the lived experience of unsheltered
people. This thesis studies and compares the results of LAHSA’s existing “hot-spot planning
process” against “hot-spot” cluster detection statistics from spatial analysis. The thesis finds that
spatial cluster detection tools identify additional areas for full inclusion in a survey sample. This
thesis also identifies environmental and demographic characteristics correlated with
homelessness and uses them to classify alternative geographies for stratification. Robust,
representative sampling for the Homeless Count Demographic Survey is important to better
understanding and serving this vulnerable, growing population. A spatial approach to
homelessness data is a major enhancement that is novel for Los Angeles County and for
homelessness policy overall.

The dangers of lead poisoning have posed a real threat to the population of the United States since the turn of the century. It has a cumulative effect in the human body and can therefore build up over time, even with low dose exposure. Children are especially susceptible to lead exposure due to their increased absorption rate of the metal and the lasting health issues that can persist throughout their lives. Minority communities with low socioeconomic status are especially vulnerable to exposure because they are more likely to live in close proximity to lead pollution sources, older homes, and have lower rates of toxicity screenings. Poisoning occurs primarily when lead is ingested through lead-based paint, lead contaminated water pipes, dust, and soil. Older cities across the United States are particularly prone to have populations with increased blood lead levels because lead was a common building material in the early 1900s. Milwaukee, Wisconsin is one such historical city where around 40% of the city’s active residential water service lines are constructed of lead. This study quantifies how many people are at risk for lead poisoning based on the existence of lead service lines in their buildings by census tract. Given the deeply segregated history of Milwaukee, an issue that still plagues the city to this day, this study also examines the relationship between the number of at-risk people per census tract and a variety of socioeconomic indicators. Dasymetric mapping techniques as well as regression analysis were used to shed light on this environmental justice issue in Milwaukee. Results show that the number of at-risk people in a census tract has a positive linear relationship with the race, education level, and poverty status of neighborhoods. In the context of Milwaukee’s demographics, the issue of lead exposure due to LSL disproportionately affects poorer communities of color.

From its early beginnings from the parent genus Homo in Africa, the species Homo sapiens
spread across the globe to every continent except Antarctica, long before the advent of large
seafaring vessels or even the wheel. The dispersion of the first Homo sapiens occurred when
other early human species, such as Neanderthals or Denisovans, were still in Europe and parts of
Asia, and land features and climates were very different from northern and eastern Africa. As
early modern humans encountered these new environments and possibly other, earlier peoples
over centuries of migration, adaptations occurred, and new cultures arose. These migrations are
of great interest to several disciplines, including physical anthropology, archaeology, and
genetics. A global geodatabase as a repository of spatial and genetic data to facilitate Spatiotemporal models of models and various data visualizations would serve all these disciplines.
Such a geodatabase also can incorporate other related data for investigation, such as global
regions, early coastlines, glacier limits, or the overall continental geography of earlier ages for
investigation of their possible effects on movement or settlement of ancient peoples.
Additionally, a geodatabase offers many options to share or limit access to data. This project
offers a comprehensive data source and tool for creating and sharing analyses with other research
efforts.

Galapagos Island’s current agricultural system of monocropping, massive food imports, and
a booming tourism sector has provided an increase in income for most galapaguenos that reside
in the island but has been deemed unsustainable by the UNESCO World Heritage Organization.
The tourism-driven urban development and monoculture system of food production have
contributed to declines in water, wildlife habitat, soil quality, and an overall loss in biodiversity.
This tourism sector growth along with a reduction in agroforestry production has reduced the
income diversification potential for galapaguenos that reside in the islands and continues to
threaten biodiversity. The most notable and critical of these global initiatives around biodiversity
is goal seven of the global Millennium Development Goals (MDGs) of the United Nations (UN)
which has since been translated into goal fifteen of the revised Sustainable Development Goals
(SDGs). The goal seven of MDGs was targeted at ensuring environmental sustainability and
parts of this goal were eventually folded into goal fifteen of the SDGs targeted at restoration and
promotion of sustainable use of terrestrial ecosystems or life on land. These targets for goal
fifteen have yet to be achieved. The scenario-based fuzzy modeling study was designed to
support organizations focused on land use planning and management for agroforestry production
and tourism development within the Galapagos Islands agricultural zone of San Cristobal, Santa
Cruz, and Isabela utilizing a site suitability analysis framework. The framework was developed
based on (1) contextual ecosystem requirements, (2) proximity to built environment
infrastructure, and (3) availability of data. The framework implementation identified scenario
1:agroforestry production as being suitable across 20 percent of the study area or 12,386.40 acres
and scenario 2: tourism development being 58 percent suitable within the study area or 35,920.56
acres.

California Opportunity Zones (COZs) provide tax incentives to investors and developers who are interested to build housing units containing affordable housing (AH). The population in California continues to increase while the cost of housing has increased significantly due to high demand. Low-income populations lack access to AH and this contributes to the financial burdens of residents who are working but cannot afford where they live. The cost of homes and rent is rising faster than the wages earned by those who work full time at minimum wage. This thesis analyzes demographic data from the U.S. Census and other sources using geospatial and statistical methods to propose new COZs in the cities of Bakersfield, Los Angeles, and Palmdale. The ideal locations for newly designated COZs would have lower population densities than much of the city of Los Angeles and labor opportunities that do not require higher education or training and that can be accessed via public transportation. The geographic placement of AH is critical to the potential positive impact on low income communities. A weighted overlay model was used to determine site suitability within each city based on features related to desirable characteristics. The new zones will provide economic opportunities for residents, alleviate problems related to population density and overcrowding, and improve the quality of life for not only low income populations but also the cities as a whole.

As our country grapples with the long term negative effects that traditional electrical generation
methods have on the environment, such as nuclear with a 50 year average decommissioning
time, natural gas and the methane emissions associated with it, and coal which is not clean, there
is a renewed focus on solar energy. This renewed focus is partially fueled by advancements in
photovoltaic cell technology and favorable regulatory conditions, resulting in a decrease of solar
energy production costs. This has led to the installed solar energy production capacity of the
United States to grow from 7.33 gigawatts in 2012 to 51.45 gigawatts in 2018. As the industry
matures and solar energy is adopted in new markets, the available land suitable for development
has subsequently been reduced. A result of this is the industry has shifted its focus to identify
suitable sites in areas that have been otherwise overlooked or discounted. To remain competitive,
potential sites must be screened to identify site conditions that can increase costs or render a site
undevelopable. This project identified that OBIA can successfully be used to identify a multitude
of features that are encountered during the development of USSE projects, but the complexity
and variability of the process makes it currently unsuitable to be deployed at scale. OBIA can
however, be used to assess current site suitability analyses by generating otherwise unknown
attribute data about a site to target locations wherein to look.

Snow crab, Chionoecetes opilio, is the largest commercial crab fishery in Alaska. Populations in the eastern Bering Sea have fluctuated over space and time, challenging statisticians attempting to model their distribution and predict stock trends to support sustainable management decisions. Climate change contributes to model uncertainty due to increased environmental variance and subsequent shifts in species assemblages adapting to changing conditions in the region. This research applied statistical toolkits and visualization techniques in GIS for spatiotemporal analysis of snow crab distribution in the eastern Bering Sea over thirty-seven years (1982 – 2018). The National Marine Fisheries Service standardized bottom trawl survey provided a robust dataset to statistically explore spatial and temporal patterns and relationships between snow crab abundance in terms of catch per unit of effort to sea temperatures, depth, and Pacific cod abundance. The temporal correlation in abundance patterns between snow crab year classes or cohorts was tested using exploratory regression and geographically weighted regression was used to visualize the nature and scale of relationships within the survey region. Overall spatial patterns of snow crab distribution in the eastern Bering Sea reflected large scale warming trends and contraction of the population to the north towards the Bering Strait. No significant relationship was found between snow crab and Pacific cod distributions on a global scale but there was evidence of a local scale inverse relationship in the southern survey region. In absence of favorable bottom temperatures in 2018, snow crab distribution displayed a greater depth dependence in the northernmost region. Temporal correlation was detected between age classes of snow crab, suggesting connectivity between maternal cohorts and progeny. These results identify local and global scale distribution trends which will support better predictive models for fisheries.

The majority of the world’s poorest populations live in areas where the road network is
unmapped, leaving them isolated from help during disasters. The Humanitarian OpenStreetMap
Team (HOT) aims to solve this problem through volunteered geographic information. Although
Humanitarian OpenStreetMap Team volunteers have traditionally digitized new roads manually,
the organization is collaborating with Microsoft and Facebook AI to explore the application of
machine learning to computer-assisted mapping workflows. As interest in machine learning
applications grows within the humanitarian community, new organizations and volunteers may
seek to carry out road classification projects for poorly-mapped regions. This thesis evaluates
the major considerations of a classification workflow – primarily training data engineering and
model selection. The study showcases the implications of training and model decision on a
subset of the SpaceNet Road Network Detection dataset using five models: Random Forest,
Support Vector Machine, Maximum Likelihood Classifier, UNet, and DeepLabV3. Models are
evaluated in extensive experiments, assessing the impact of varying width of the road features
used to label the training data. Each scenario is evaluated using a Matthew’s Correlation
Coefficient, confusion matrix, and visual inspection of the predictions. Based on these results,
the project reports valuable lessons-learned that can inform humanitarian organizations’ design
for road classification workflows.

Due to technological advances, wind energy is now a commercially viable input to the electrical grid. However, siting constraints of wind resource availability, regional economics, community concerns, issues with grid integration and ecological concerns are hampering wind technology’s market penetration. This thesis seeks to address these issues by using multi-criteria site selection to demonstrate how a volumetric wind turbine can be used for successful siting of utility scaled wind turbines in urban environments. The urban environment for this study, Fort Worth, Texas, is composed of land use types and zoning restrictions some of which impose exclusion criteria that restrict most of the area of interest from the analysis. Fortunately, there are areas zoned for industrial, agricultural, and other land uses that are compatible for the siting of wind turbines. Furthermore, this research used the spatial inputs of property values, terrain, nearness to electrical infrastructure and wind resource availability to create a weight schema that can identify the best sites in the study area. The results show that a utility-scaled wind turbine could be sited within the city having a wind power ranking of marginal or greater and that has undeveloped open spaces, industrial zones, and areas such as landfills and brownfields that impose limitations to future development.

Hurricanes and heavy rainfall continue to cause flooding events. There is a need for researchers and other organizations to develop web and mobile applications to assist with flooding by studying vulnerabilities, traffic congestion, and decision making to evacuate or shelter in place. The Atlanta Metropolitan Area has not experienced a flooding event in nearly 10 years. Still, if a flood occurred, it could be as catastrophic as Hurricane Harvey was in Houston or Hurricane Katrina was in New Orleans. ❧ The Atlanta Metropolitan Area experienced a flooding event in 2009 that caused the displacement of almost 17,000 residents and resulted in the death of 10 people. This event motivated the development of a web application that could help users prepare before a flooding event. The application enables preparedness by allowing users to view the built and social environments in areas affected by previous floods on their mobile device or PC. By allowing the mobile device or PC to access their location, the user can view nearby shelters should evacuation become necessary. This application has the potential to bridge the communication gap between federal, state, and local officials, emergency responders, and the public before a flooding event. ❧ This application has the potential to reduce loss of life and help with planning responses to future flooding events by identifying nearby shelters, and eventually helping individuals to develop ways to protect their homes and businesses from flooding.

Connectivity is important for biodiversity conservation because it can offset the impacts of habitat loss and fragmentation, allowing migration, dispersal, and adequate gene flow. Barriers that cut across a species range such as the United States-Mexico border wall can block dispersal and negatively impact gene flow between populations. It is therefore important to understand how to establish or re-establish wildlife corridors in order to help species survive. The focal species selected for this thesis project was the jaguar (Panthera onca). The study area comprised several ecoregions that covered portions of the United States of America (US) and Mexico. The jaguar’s suitable habitat was identified using a Random Forest model to predict potential habitats. The factorial least-cost path analysis was used to identify the jaguar’s potential corridors. Results predict there is good habitat for jaguars in the Sonoran-Sinaloan subtropical dry forest, Sinaloan dry forests, Sierra Madre Occidental, California montane chaparral and woodlands, Arizona Mountains forest, Sierra Madre Oriental pine-oak forests, Veracruz moist forests, Sierra de la Laguna pine-oak forests, Sierra de la Laguna dry forests, Tamaulipan matorral, and small portions of the Sonoran desert ecoregion. The jaguar's potential corridor modeling suggests that there were previously two high-density corridors between the US and Mexico allowing jaguar connectivity. However, if the partially constructed border barriers are completed those jaguar corridors will be lost. Work on nine co-distributed mammals (orders: Carnivora and Artiodactyla): jaguar (Panthera onca), mountain lion (Puma concolor), ocelot (Leopardus pardalis), bobcat (Lynx rufus), black bear (Ursus americanus), gray fox (Urocyon cinereoargenteus), Mexican gray wolf (Canis lupus baileyi) Sonoran pronghorn (Antilocapra americana sonoriensis), and Bighorn sheep (Ovis canadensis) in the US-Mexico border ecoregions will continue after the completion of this work.

Traditional sources of energy are outdated and destructive. There is a clear need for new and more sustainable energy sources, creating a huge market for innovative research and technology within the energy industry. The use of the world’s oceans to generate energy is pioneering and provides many enticing benefits including cleaner and more consistent energy generation. Tidal energy converters harness the power of moving water generated by the tides and is a huge source of untapped energy. This study analyzed the parameters most important to successful tidal energy generation in order to determine the most suitable sites for implementing this technology in southern California. Using data from various sources, two different interpolation methods were used to create a continuous raster surface of tidal range values that, along with other variable fields related to tidal energy, were used to perform two different types of analysis, a weighted overlay and fuzzy overlay analysis. The results of these two different analysis methods were compared and this methodology provided a conclusive map showing the most effective sites for successful use of tidal energy converters in southern California. An analysis was also completed to look at the distance of suitable sites to nearby onshore energy facilities. This research builds upon previous renewable energy studies in southern California and provides insight into how the region can reduce its fossil fuel emissions and convert to cleaner, more sustainable energy sources.

Past chemical emergencies in the United States prompted the initiation of a variety of toxic substance and pollution control programs and regulations, including the Emergency Planning and Community Right-to-Know Act and the Clean Air Act. While these have produced decades-worth of valuable pollution datasets, they are stored on a government website in a collection of CSV tables. This method of accessibility is largely incompatible for analysis due to the static nature of tablesㅡthe need to download them locally and appropriately query them to extract relevant data. For analysis, data is best visualized with dynamic tools and within interactive environments. ❧ This project focused on the public’s right-to-know about toxic chemical releases in their community by developing a geospatial web application called Cal ToxTrack. Built from scratch using PostgreSQL as a database, GeoDjango as a Python development framework, and Leaflet as a JavaScript framework, it effectively visualizes chemical releases and provides interactive tools to help explore pollution data. To ensure that the application does not depend on access via state and federal governments or whether the developer has continued access to commercial products, this application developed entirely from the backend database through the front end and interface, otherwise known as full stack development, relies on publicly-available pollution datasets downloaded and hosted by an API created by the developer and was entirely created using open source tools. With Cal ToxTrack, users can utilize a map and spatiotemporal tools to visualize what chemicals have been released, to what magnitude, and where; practicing their right-to-know.

Tropical landscapes in Costa Rica have increasingly become targets of restoration efforts after deforestation depleted 90% of the region’s forests by the end of the 20th century. Research has shown that the environment surrounding a restoration site influences outcomes in fragmented landscapes, particularly as to the amount of forest cover surrounding restoration areas. However, the degree of influence that forest cover has on restoration sites and the long-term effects have historically been understudied due to the difficulty in assessing forest cover in remote regions through conventional field methods. As a result, there is a need for more time and cost-effective ways of evaluating and understanding forest cover change within the context of restoration efforts in remote areas. ❧ Geographic Information Systems (GIS) and remote sensing technologies have been utilized by researchers to understand better the relationships between abiotic and biotic factors in ecosystems. This study analyzed forest cover changes from 2005 to 2014 using high-resolution remote imagery to understand how forest cover changed surrounding 13 restoration sites near Las Cruces Biological Station (LCBS). The forest cover analysis revealed that the study region experienced a 9% net increase in forest cover over nine years. Similarly, all except one of the restoration sites had a net increase in forest cover within 200 meters. Topographic variables were extracted from a 5-meter DEM to understand their influence on the changes in forest cover. We hypothesized that elevation, slope, aspect, and distance to restoration site would have a strong and positive correlation with whether areas surrounding the restoration sites reforested from 2005 to 2014. A regression analysis revealed that topographic factors do not solely explain the variations in forest cover gain between sites; However, aspect, elevation, and distance to the restoration sites center had a significant impact on forest cover gain in the study sites.

Los Angeles has a homelessness crisis. The city has long struggled to meet the needs of the growing homeless population, and the problem continues to amplify as the most recent 2019 Point-In-Time (PIT) Count shows an increase in homelessness. The Department of Housing and Urban Development (HUD) Continuum of Care (CoC) federal grant program establishes regional or local planning bodies to coordinate housing and services funding for homeless people in an effort to promote an integrated system of care. As a local planning body, CoCs address the issues their local communities and have the potential to affect positive change. Access to healthcare is one such issue facing homeless populations that the LA CoC could better address using spatial analysis, namely where homeless populations reside in the CoC boundaries relative to established hospitals and medical facilities. ❧ This project used a geographic information system (GIS) to assess the state of homelessness in the Los Angeles CoC as of June 2019. A population distribution and density analysis was conducted, indicating that homeless populations tended to be larger and more concentrated in the census tracts comprising downtown Los Angeles and Santa Monica. To determine the degree to which homeless individuals can access hospitals and medical facilities, an accessibility analysis was conducted using a modified two-step floating catchment area (2SFCA) methodology. The 2SFCA accessibility index indicated that census tracts within the downtown area had homeless populations within a 1-mile distance of at least one hospital as opposed to more rural tracts that tended to lack any access. However, access to medical facilities within a walkable distance varied in the downtown census tracts. Recommendations for funding allocation, the establishment of transportation initiatives, and additional medical facilities to improve access were made.

Motorcycles are disproportionately affected in collisions when compared to other motor vehicle types, leading to an increased vulnerability of injury or death to motorcyclists. Multiple factors can contribute to this disproportionate impact, including environmental factors, inattention of other motorists, driver error, and physical road characteristics. Many motorcycle safety initiatives address the error and role of other drivers in motorcycle-involved collisions but little attention is often given to the environmental and roadway factors. This lack of attention and analysis reduces the ability of transportation agencies to obtain a complete common operating picture for all factors impacting motorcycle safety – allowing for missed opportunities to decrease the increased vulnerability of motorcyclists. ❧ This project utilized Geographic Information Systems (GIS) as a tool to identify locations on state-maintained roadways showing statistically significant clusters of motorcycle involved collisions. The collision data for this report were retrieved from the Kentucky State Police collision database; filters were used to extract motorcycle involved collisions for a ten-year period from 2009 and 2018. A site suitability study was completed using the collision data and road network layers to determine sites suitable to the introduction of a motorcycle lane in an effort to increase motorcycle safety. While there are multiple strategies for reducing motorcycle involved collisions, exclusive motorcycle lanes offer motorcyclists a safe location to ride without interference from other motor vehicles in areas with high traffic flow. Spatial analysis was utilized to complete a site suitability study to determine needed and viable locations for motorcycle lanes using Livingston, Jefferson, Fayette, and Meade County within the Commonwealth of Kentucky as a study area.

While the New York City coastline protects its citizens from severe weather, it has been dramatically altered due to human and natural processes over the past century. To analyze these changes over time, two locations that have faced significant coastal erosion over the course of this century were studied: Jamaica Bay and Coney Island/Gravesend Bay of South Brooklyn. This thesis project investigates shoreline changes to these two locations using historical and current maps, to quantify how the shoreline moved from the early 1900s to now. Utilizing storm and shoreline measurement data with geographic information systems (GIS), shoreline changes were measured and compared using reference points. Additionally, records of storm severity in these areas during the same timeframe were analyzed alongside the shoreline movement data. Results from these two sites indicate that accretion has been the prime mode of coastal change throughout this century. These results are contradictory to anecdotal evidence that shorelines have significantly eroded with storm activity. During this 120-year study period, the shorelines have experienced some erosion, but overall, the shorelines have expanded. This study demonstrates, that while erosion has occurred, coastal movement is dynamic and primarily the result of human impact as opposed to natural processes.

Due to the intense pollution and warming rates, as well as other strenuous factors, future sea level rise (SLR) is projected to cause severe damage to people that live in coastal areas around the world. The population from Huntington Beach and Newport Beach, California has a high chance of suffering from the imminent impact of SLR. These two cities are particularly appropriate to a study of SLR impacts because they have low-and high-laying lands. Highly developed coast line infrastructure with high property values, and large numbers of people living near the beach. ❧ This study estimates population that may be directly affected by SLR in the two cities by using three dasymetric mapping methods and two SLR projections. The methods are centroid-containment, Filtered Areal Weighting (FAW), and the Cadastral-based Expert Dasymetric System (CEDS). The SLR projections are based on a global and local scale from the National Oceanic and Atmospheric Administration’s SLR Viewer. Geographical information systems (GIS) is utilized to digitize, analyze, and compare the most recent spatial data. The project’s first objective evaluates SLR effects on populations and neighborhoods in the two cities. Secondly, this project describes and compares results between the three dasymetric mapping methods. Lastly, the mapping results of Huntington Beach are compared to its neighboring and contrasting city, Newport Beach, for further understanding of the mapping results. This study concludes that SLR may impact the wealthy population the most in both cities. Furthermore, this research provides a method for the two cities and other coastal cities in order for them to help people that may be impacted by SLR quickly and more efficiently. Emergency response agencies can also use this research to accurately portray impacts to people caused by pollution, or natural disasters.

Despite Arizona relying on Arizona groundwater to meet a significant portion of its water needs, the locations of Arizona’s groundwater aquifers are not fully mapped, and methods to interpolate the locations of aquifers from test boreholes remain inaccurate. In response, this study implements a workflow leveraging three-dimensional (3D) interpolation to fill in that knowledge gap within a study site: the Prescott Active Management Area surrounding Prescott, Arizona. Using borehole log data and digital elevation models, the 3D extent of permeable layers are mapped, serving as proxies for aquifers and aquitards, respectively. This project makes use of Empirical Bayesian Kriging 3D (EBK3D) to interpolate permeability data in three dimensions. When tested on four random boreholes, this model correctly predicted an aquifer 80% of the time in comparison to 42% using traditional 2D interpolation. The model’s improved accuracy provides an approach to improve drillers’, policymakers’, and scientists’ understanding of the hydrologic activity in the area. Such an improvement may lead to better-informed storage models, changes in water management, and greater cost efficiency when drilling new wells.

Invasive insects are damaging to the environment and economy. Early detection of these pests is important to prevent their establishment before their populations grow and cause extensive damage. In the United States, the Animal and Plant Health Inspection Service (APHIS) safeguards natural resources and agriculture through their Plant Protection and Quarantine (PPQ) program by preventing the establishment and entry of forest pests into the United States. APHIS traps exotic wood-boring beetles (EWBB) and other pests. Throughout all the APHIS offices across the United States, there is no unified field data collection method. As of 2019, the APHIS office in Chicago, IL uses Microsoft Access for all their field data collection. The main objective of this thesis is to build a Web GIS with mobile data collection capabilities and an operations dashboard to further monitor data collection in the field. Collector for ArcGIS can be used for mobile data collection in the field and an operations dashboard can help supervisors monitor field operations more effectively. This project utilized a user needs interview with members of the APHIS team in Chicago to guide application development. The developed Web GIS application, which includes an operations dasboard and Collector for ArcGIS, was then tested by users of APHIS to determine whether their workflows would benefit. The application was well received by users and the feedback helped to uncover a few notions that could guide further development of this application in the future. These enhance APHIS’s current workflow through real-time data collection as well as more accurate data collection. The completed application could also be used in rural areas where less high-risk importers are present through customization. Approximately 80% of the application would remain the same, though there would be changes in the symbology and data collection layers. This could benefit APHIS offices, as well as other organizations monitoring invasive pest control.

Vegetation growth patterns are indicators of environmental change. However, sampling and recording landscape-scale studies over time are challenging using traditional methods. This study used remotely collected imagery of montane forests through alpine over thirty years, 1984 to 2018, to classify and examine changes in vegetation patterns. The imagery analysis methodology focused on the San Juan Mountains of Colorado. Imagery from Landsat satellites was utilized to derive normalized difference vegetation index (NDVI) values examining vegetation patterns throughout an elevation gradient. Elevations from 3000 meters above sea level to the peak of the tallest mountain in the study area (Uncompahgre Peak 4365 meters) then classified into the land cover types of the local ecozones. Ecozones examined were the nival and subnival of the alpine and the montane forest of the subalpine. The corresponding land cover types where soil and rock, shrubs and grass, and mixed forest, deciduous forest and coniferous forest respectfully as defined by the U.S. Geological Survey. When sampling of changes focused on single peaks, a slight rise in altitude over time is observed (m= 0.1754). When sampling included more broad sampling throughout the study area, an even more minor negative trend resulted (m= -0.1369). The study discusses that the broad standard array sampling type was possibly skewed due to samples being taken from across long distances with little elevation change overall throughout. The discussion includes suggestions for further research.

Natural disasters have always influenced migration, whether international or within one country’s borders. However, as the effects of climate change continue to cause irregular weather patterns and stronger, more frequent natural disasters, the number of individuals at risk of being displaced from their home due to natural disasters is poised to substantially increase. Given the millions of people on the brink of needing to relocate due to natural disasters, as well as the potential billions of dollars needed to repair the resulting damages, there exists a need to better understand trends in weather and migration patterns. Such an understanding would allow for governments and emergency response teams to be more prepared to face sudden onset disasters. The Internal Displacement Monitoring Centre (IDMC) published a dataset detailing the number of internally displaced people (IDP) per country per year between 2008-2018, and the specific natural disaster event associated with each IDP. This project utilized the IDMC dataset to create a web map application using ArcGIS Online that will organizes and visualizes the data in a spatial context. The original IDMC dataset was broken down into smaller thematic datasets using the R programming language, which were subsequently visualized using the ESRI products ArcGIS Pro and ArcGIS Online. The application was designed for ease of use, thus allowing for new trends and potential patterns to be discovered far more easily. The resulting web application includes widgets and tools that allow users to manipulate the dataset in meaningful ways unique to their needs.

Systemic racism, institutional racism, structural racism: these are the terms used to describe unequal minority participation in job markets, over representation in the criminal justice system, and lack of access to and enjoyment of clean and safe neighborhoods. Studies in social justice and environmental justice are now starting to quantify structural racism by utilizing Geographic Information Systems and applying analytic methods of Geographic Information Science. One area ripe for study of structural racism is whether race-neutral laws and regulations promote race-neutral distributions in the built environment or perpetuate existing structural racism. ❧ The distribution of alcohol retailers in Orange County, California, provided an opportunity to explore how a race neutral regulation—in operation for over two decades—has impacted the built environment. Exploring the distribution of alcohol retailers informs our understanding of structural racism because a higher density of retailers has been correlated with negative impacts on neighborhoods such as increased crime, negative health outcomes, and poverty. Moreover, California’s alcohol licensing regulations are race-neutral and as such do not consider race as a factor in determining the approval or rejection of a license application. ❧ This study analyzed the February 18, 2020 inventory of active off-site retail sales alcohol licenses in Orange County and compared the distribution of licenses with race/ethnicity across the county. The comparison was repeated at two spatial scales: census tract and a scaled population grid based on the Oak Ridge National Laboratory’s LandScan 2018 dataset with 30 arc-second cells (∼0.5 miles). This study found that Hispanic populations were consistently overrepresented in census tracts and cells where alcohol licenses were found. This result suggested that requiring laws and regulations to avoid recognition of race is insufficient to ensure race-neutral distributions of benefits and detriments in the built environment.

Wetlands are a unique and important ecosystem for our world by serving as one of the largest forms of carbon sequestration and storage while also housing thousands of plant and animal species. As much of Earth’s wetlands are disappearing due to human activities, conserving these natural resources has become even more crucial. Restoration, or the process of returning a degraded area to its original form, is necessary for the future of wetland ecosystems, as well as our world. In the conservation field, unmanned aerial vehicles (UAVs) are a common tool for wetland assessments. However, they are rarely used for restoration planning, which is mostly done on a larger scale using LiDAR and satellite imagery. Because wetland restoration relies on noting small changes, the flexibility of UAVs may prove to be a more useful tool. As ecosystems that connects land and water, vegetation and hydrology can vary intermittently and may require detailed planning and consistent monitoring. Although other forms of remote sensing can give us accurate DEMs and high-resolution imagery on a large scale, a UAV may be more effective for smaller study areas. The 60-acre Kahanaiki restoration area is an ideal study area for restoration planning with a UAV. This study utilized a DJI Phantom 4 Pro V2 drone to acquire high-resolution imagery and a 3D point cloud, which was then classified into variables – streams, mudflats, plant species, urban land use, and a 0.15-meter DEM. These criteria were used in a suitability analysis to determine where restoration efforts are most likely to succeed. Along with this, high-resolution imagery of Kahanaiki and 2 other current restoration sites were created for use in future monitoring. The purpose of this study is to assist with conservation research of the Kawainui Marsh by monitoring existing restoration areas and planning ideal locations for future restorationsites. In doing so, the research determined if UAVs can be an effective tool for restoration planning for future wetland mitigation

The Western Monarch butterflies of North America have seen a steep decline in population over the last two decades due to climate changes and land cover changes in the overwintering sites, and the decline of milkweed plants in the breeding grounds due to pesticide glyphosate application. According to a study by the Center for Biological Diversity, over the last two decades, monarch populations overall have declined by 68 percent and the western monarchs have seen nearly 90 percent decline, reducing their overall population from more than one billion to two hundred odd million (Giffiths & Villablanca, 2015). ❧ This project aimed to quantify the land use land cover changes, trend of pesticide Glyphosate application from 1990 to 2015, weather changes from 1990-2015 in the Monterey county, the study area for this project. The land cover and land use change were analyzed using Landsat ARD imagery of 1994, 1998, 2002, 2006, 2014 and 2018, using NDVI (Normalized Difference Vegetation Index) differencing method. The NDVI image for each year were classified into 5 categories based on the spectral index and the results indicate fluctuation in the land use land cover, however, there is not significant change in any class. The pesticide Use Report showed Grapes and uncultivated agricultural land used high amount of pesticide glyphosate each year. The application had increased from 1996 to 2000 and again it is increasing from 2004. The weather data analysis did not show any inclement weather pattern, which might be unsuitable for monarch winter survival.

Urbanization and other anthropogenic developments have changed the environments we live in. One of the effects of urbanization is the Urban Heat Island effect, a phenomenon where urbanized areas experience higher temperatures than surrounding rural, less developed areas. While an urban climatic map can be a useful tool for understanding the Urban Heat Island effect, there is not consistent methodology for its creation and implementation in urban planning. This reduces its utility in informing policy and decision making in Urban Heat Island mitigation efforts. In response, this study demonstrates an approach for the creation of accurate urban climatic maps, which can be replicated for all of California. Using the city of Los Angeles as an example, this approach successfully produced different urban climates, and estimates how each urban climate affects the Urban Heat Island effect. The urban climatic map overlays various classified layers from multiple fields, such as urban planning and climatology, to construct the climatic classes for the city. These classification values are based off of the values used in the Hong Kong urban climatic map. Each climatic class is a description of the thermal load and dynamic (air movement) potential that is experienced in each area of the city. Classes with high thermal loads and low dynamic potentials can be identified as areas experiencing a more intense urban heat island effect. Using the same methodology, the creation for additional urban climatic maps, or regional climatic maps is possible, greatly improving regional efforts to mitigate Urban Heat Island effects across California.

The Bureau of Land Management (BLM) is instrumental in connecting people with public lands by providing and protecting opportunities to enjoy and use our country’s resources. Understanding the cumulative effects of resource management programs is crucial for decision makers to develop effective land management practices and appropriate allocation of funding and resources. A comprehensive, standardized, and transparent GIS workflow can help visualize and analyze ecological integrity, landscape patterns and processes, and promote a consistent Cumulative Effects Analysis (CEA) and collaborative management across jurisdiction boundaries. ❧ This research evaluates the cumulative impacts of resource management programs in the BLM Carlsbad Field Office (CFO), New Mexico by incorporating ecological integrity indicators, resource- and stressor-based metrics, and landscape metrics to create a Landscape Integrity Index (LII). Two resource management programs, Vegetative Communities and Minerals – Leasables – Oil and Gas, were selected as the programs of interest for this study. The LII model considers the management goals and objectives in the Draft BLM CFO Resource Management Plan (RMP) to identify the necessary indicators and metrics. These indicators and metrics were each scored for their site impact, distance decay function, or landscape metrics through the use of a Composite Scoring System, and then combined into a single map. The resulting map with the LII values shows areas of low landscape integrity near the urban and agricultural areas in CFO planning area and high landscape integrity near central and southwest corner of CFO. CEA practitioners and land managers will be able to address management goals and objectives, conduct a more systematic and consistent analysis with relevant indicators and metrics, and visualize landscape integrity using the LII framework.

This study was undertaken to determine if gaps in health care accessibility existed in Los Angeles County. A primary consideration of this study was the veteran population in Los Angeles County and their accessibility to healthcare. Accessibility is defined by the Veteran Administration (VA) as the acceptable travel time to the nearest VA healthcare center for a veteran to receive desired care. As part of the MISSION (Maintaining Internal Systems and Strengthening Integrated Outside Networks) Act of 2018, veterans may receive primary care outside the VA system if the average drive time to a VA facility is thirty minutes or more. This thesis examines the spatial accessibility for veterans to travel to VA facilities instead of accessing care outside of the VA system. At this time, there are three VA medical centers and seven primary care facilities located throughout Los Angeles County. This study analyzed the areas around the three medical centers and seven primary care facilities and identified gaps in accessing health care based on drive time using the enhanced two-step floating catchment area (E2SFCA) method. It identified where gaps in spatial accessibility exist using veteran estimations at the census tract level extent. The study found that gaps in coverage exist in the eastern area of Los Angeles County. The methodology and detailed analysis can serve to determine differences in drive time distance decay for veterans to access primary medical care in other locations throughout the country.

GIS Capabilities are rapidly expanding into the web and cloud environments, but there is little research on the capabilities and performance of 3D web GIS exploitation systems. To evaluate current 3D GIS capabilities and performance within the web, Esri ArcGIS Enterprise Portal, Cesium JS, and Hexagon Geospatial Luciad RIA were all configured on a cloud-based Amazon EC2 instance to host and serve 3D tile datasets that implement adaptive tiled data structures. Using two different source point cloud datasets, a high-resolution photogrammetric dataset, and a lower resolution lidar dataset, resource loading time and resource memory was tested within each system with increasing overall tileset sizes and with three different levels of zoom. The results show that while Cesium JS is quickest, Esri ArcGIS Enterprise Portal performs similar and with more detailed visualizations for both datasets. Hexagon Geospatial Luciad RIA performed slower than the other two systems, but possesses the most photorealistic and detailed rendering of the systems. Performance differences between the servers can be seen in the level of library compression and number of libraries imported into the page. Cesium JS is generally quickest, but most compressed and lightweight server. The larger detail and loading time in Esri ArcGIS Enterprise and Hexagon Geospatial Luciad RIA can be traced to smaller levels of compression and more library imports to enhance detail of 3D data rendering. Overall tileset size and spatial resolution of data did not significantly impact performance while zoom level did significantly impact performance. Generally, higher resolution of zoom required more resources and loading time. Results indicated that difference visualization systems are best suited for different applications. Cesium JS would likely be most suited for complex analytic operations, while Hexagon Geospatial Luciad RIA would be best for detailed single scene visualization.

The number of K-12 educators utilizing Geographic Information Systems (GIS) is on the rise. As more tools become available, through companies such as Esri Geoinquiries, Google Maps Treks, and Esri Academy, an ever-rising number of educators employ such tools in their classrooms. This thesis provides a model that educators can use to 1) synthesize the delivery of content in tandem with GIS, 2) ensure adherence to standards-based instructional requirements while using ArcGIS Story Maps, and 3) teach secondary age students to use GIS itself. The case study on which the thesis was based was a template for an ArcGIS Story Map that can house traditional classroom content and GIS-enhanced resources while adhering to national, state, and local student learning outcome standards, as well as incrementally increasing the students’ understanding and use of GIS. The course that was the case study covers eastern hemisphere geography and is taught primarily to freshman in a high school in Meridian, ID. The ArcGIS Story Map was created using the Classic Map Series template and organizes each map around a region of study in the class (e.g. the Middle East, North Africa, etc.). The content of each regional map was based on standards which are linked to student learning outcomes associated with a specific theme (e.g. culture is the thematic focus of the Middle East and North Africa unit). Enrichment content in a variety of multimedia formats was embedded within the content of each region. In addition, each successive regional map asks the student-users to utilize increasingly advanced GIS skills and proficiencies. A survey was fielded to gauge the attitudes of other educators as to the effectiveness of this approach as well as the extent to which they might adopt this approach in their own classrooms. Survey data showed that educators were receptive to this approach and were more likely to adopt it after viewing this application.

The Los Angeles County Metropolitan Transportation Authority is the third largest public transportation agency in the country—established in 1993 due to a merger between Southern California Rapid Transit District and the Los Angeles County Transportation Commission. The mission of the company is to enhance the “quality of life for all who live, work and play within LA County” (Metro’s website). In order to achieve this mission Metro has created services and has invested in bikes, buses, light rails, and rideshare programs. The most impactful investment was into the light rail systems. This rail system includes two rapid subways, four light rails, and ninety-three stations, which spans 105 miles throughout the county. This thesis examined how Metro’s light rail system has influenced the value of single-family residential homes in the neighborhoods surrounding the Expo (South Los Angeles) and Gold (East Los Angeles) lines. This study examined how Metro has transformed the zoned area near these two Metro light rails. In order to analyze the impact, the Hedonic Regression Model was utilized to determine the correlation between these light rails and housing values. A series of maps were created to depict the demographic and physical change over a ten year period. The result of the study showed that the impact that Metro’s light rails was minimal and did not show significant change in the social, economic, and structural landscapes of these communities.

The lack of seafloor information is often a result of the challenging logistics and expenses involved with acquiring data in this unique environment. Yet, despite the sparsely sampled environment, many significant efforts exist to create global bathymetry models. However, there exists a public misunderstanding of the true sampling density in the ocean that can be largely attributed to contemporary interpolation and enhanced cartography. The seafloor is more sparsely sampled than most people realize. Thus, it is important to understand the influence of the underlying source data and the interpolation technique used when creating an accurate digital bathymetry model. The accuracy of a surfaces can depend on sampling density, interpolation method, and local geomorphology. However, if a bathymetry surface can be accurately created using sparse measurements, mission planning can be directed to sample the seafloor at a certain resolution. The results of this thesis research encourage future exploration of a computationally efficient method to assess the best method of interpolation method in different regions under different conditions.

Commercial UAS operations are one of the fastest growing industries in the world, exceeding 127 billion dollars per year as of 2016. The exponential growth combined with the relative lack of regulation over the last few years has highlighted the struggles of government to keep up with regulating a dynamic industry. With companies looking to perform beyond visual line of sight (BVLOS) operations over large areas, the remote pilot(s) in command (RPIC) may have to choose places to launch or recover their aircraft without being able to visually perform an initial site survey. There is no formal training apart from actual real-world experience that can prepare a RPIC for landing zone (LZ) site selection for BVLOS operations even though it is one of the most critical factors to the success of an unmanned flight operation. GIS-based approaches for planning, especially with BVLOS flight operations, is crucial to the future of the industry. This approach utilizes three use cases. Two of the use cases (transmission lines and railroads) are linear in nature while the third (wind farms) is non-linear in nature. Current approaches that are utilized are using manned aircraft, choosing landing areas in situ without prior planning, or ignoring regulations altogether. The last approach is rarely used negligently, but instead results from a lack of knowledge regarding regulations. Results show this approach to LZ planning is superior to existing practices in ensuring compliance and project efficiency. BVLOS operations are increasing exponentially, and advancements such as these demonstrate benefits for a variety of commercial applications.

People use parks and facilities for different social, economic, and physical reasons. Access to parks enables a higher quality of life for those who can visit them (Wolf 2017). Park accessibility studies have stressed the importance of park accessibility as a component of environmental justice (Park 2017). ❧ The broad goal of this project was to help reduce the disparity between people with little access to parks and those who do have ample access to park space and amenities. The objective of this specific project was to provide decision makers with access to park information and analyze the accessibility of neighborhood parks in Redan, Georgia. The outcome was a web application with an accessible interface that allows stakeholders to conduct analysis of neighboring parks on their technological devices. Through this application, entitled “Park Friendly-Redan”, members of the community, park directors, and local officials can learn about neighborhood parks and share insights on recreational facilities and upcoming park projects. Analytic methods included dasymetric mapping, which plots the population using parcel data and a network analysis, resulting in a tool that creates service areas based on specified road distances. The analysis was integrated into an interactive web application. City developers and park planners that design future park projects will have the opportunity to use the tools needed to run real-time analyses and gather input from community residents regarding parks in their neighborhood. Future goals include a volunteered geographic information (VGI) system where users input information on facilities and add community concerns. It is hoped that the outcome of this project will promote user engagement and lead to a deeper discussion within the community regarding park goals and accessibility.

A bridge between spatial science and financial analysis has not yet been built, and this research lays the foundation to build this bridge using the PESTELM (Political, Economic, Sociocultural, Technological, Ecological, Legal, and Militaristic) framework, the analytical power of a Geographic Information System (GIS), equity valuation models, and visualization through a web and mobile application. This study introduces the concept of equity asset valuation, describes the PESTELM framework, application development (of both the web and mobile applications), the methodology to combine a real-time analysis with live datasets, and describes the process of using spatial analyses outputs as inputs to an equity asset valuation model. Lockheed Martin Corporation is used as the equity asset valuation case study to quantify how PESTELM datasets affect overall company valuation. The results of this application development and spatial financial analysis describe the process of using real-time analysis on live datasets and how static analyses outputs can be used as inputs to a valuation model that uses real-time financial data through Google Finance. This research is a basis for the intersection between spatial sciences and financial analysis—and as such provides a recipe to combine the disciplines.

The Angeles National Forest (ANF) contains miles of backcountry hiking trails easily accessible to residents of Los Angeles. Those who are planning to go on a hike have several options to aid in their planning process from guidebooks, to online resources, to mobile applications. These resources often include trail descriptions, directions to the trailhead, and statistics regarding each hike, which sometimes includes a vaguely defined difficulty rating. However, not every hiker experiences trail difficulty the same way. New hikers are unlikely to agree with experienced hikers as to the difficulty rating for the same hike. Similarly, different hikers may disagree on what exactly makes one trail harder than another. ❧ To address this, this project developed the Angeles Hike Finder (AHF). The AHF allows users to identify which factors they consider to be most difficult, and receive a customized output of trails in the ANF that meet the search criteria. This application was developed from a manually generated trail database of hiking trails in the ANF, containing trail data regarding trail distance, elevation gain, maximum elevation, and trail type. Once the database was developed, the AHF was created using Esri Web AppBuilder. The AHF allows users to determine what range of attributes or difficulty ratings they are interested in before the application generates the customized trail listing. The goal of this application development project was to address the lack of defined difficulty ratings for trail planning present in other hiking applications. Through the creation of the AHF, hikers in the ANF can see what is perceived as most difficult for their unique needs. The AHF can serve as a template for how other hiking applications address the concept of the difficulty for hiking trails.

All professions work with limited resources, and archaeology is no different. Archaeologists were among the first adopters of GIS technologies, thanks to the field’s focus on the spatial distribution of human populations and other location-based information. In recent years, advanced surveying and analysis techniques have boosted the capability of archaeologists to both discover and catalog newfound relics of ancient civilizations. Many agencies that claim jurisdiction over large tracts of land have begun utilizing GIS software to build predictive surfaces from previously gathered spatial data, streamlining their practices of searching for new archaeological sites. ❧ This thesis project aims to quantify Greek settlement patterns using Maxent modeling and use the results to create predictive surfaces in the Peloponnese, a peninsula in Greece that is home to some of the greatest of the Ancient Greek civilization. I chose this area, and this civilization, as it marks one of the most influential in the context of Western development; its contributions range from mathematics to drama to politics. My goals were twofold. First, I identified important environmental variables in the establishment of Ancient Greek settlements, looking at proximity to water sources, proximity to the coast, land cover, underlying soils, elevation, slope, and aspect. Second, using the variables identified, I created a predictive surface for ancient settlements in this region. I expanded on the existing literature by including proximity variables as walking distances, which are more relevant to ancient civilizations than a simple Euclidean distance. ❧ This study serves as a proof of concept of the validity of including walking-time variables in archaeological analysis. These could be utilized in other areas for similar diagnostic and predictive purpose.

Glaciers are an important indicator of global and local climate change, an important factor in regional environmental health, and a possible natural hazard. The importance of glaciers has led to the creation of many glacial inventories ranging in scale. The datasets from these glacial inventories are often outdated or incomplete, especially for those in California. Additionally, none of the glacier inventories track change in the glaciers over time, but rather they are limited to the data at a single point in time in California. The objective of this project was to create a web-based relational geodatabase and web map to track spatiotemporal information of glaciers in California. The geodatabase was designed with three user groups in mind: geologists and glaciologists, policy makers, and the general public. The geodatabase was also designed to meet several goals. The first was to archive and store the large amounts of glacial data collected by other sources, add temporal attribute onto current glacial data, and plan for new types of data. This was done by developing the database in such a way that it meets current requirements and standards of existing global glacial databases. Additionally, another goal was to create a web map that allowed the data to be easily accessible and useful for various users with different goals. A web map was constructed to display the data within the geodatabase and allow the user-groups to interact with and download the data. The results of this project including the geodatabase, data, methods, images and documentation are published at mapping.cool/california-glaciers and on GitHub, along with the web map for public use. This geodatabase and web map can be used for different applications, ranging from the simple uses like as monitoring glaciers to detect change, to more advanced use of the data such as hazard detection and assessment.

Alaska is home to an extensive network of hiking trails that navigate a remote and diverse wilderness. Mapping applications are currently available to trail users, but each available option lacks either spatial extent of coverage, accuracy of data, or detailed descriptions of the conditions on the trail. Users can spend significant time researching descriptions and current trail conditions across websites and mapping applications in order to make proper decisions for planning routes and equipment. The website Alaska Hike Search (AHS) provides detailed descriptions of popular hikes across the state but lacks an in-depth mapping application for searching trails. The lack of information on current trail hazards and wildlife activity compounds the concerns for proper trip planning, creating concerns for the safety of hikers and other trail users. ❧ In this project, a web application was designed as a prototype for the AHS website that also allows for contribution of volunteered geographic information (VGI) of trail hazards and warnings. This project gathered spatial data available from the agencies and organizations that manage trails, with a focus on the 129 trails described on AHS. Where authoritative data was not available, spatial and feature data was collected from other hiking trail applications to complete the database. Alongside the creation of this database, a web application was designed through hypertext markup language and the ArcGIS Application Programming Interface for JavaScript 4.14. This web application allows users to discover trails near them, search for trails by name, and filter trails that meet their desired difficulty. Through an editable VGI dataset, users can provide hazards as point features for warnings to unsafe conditions, or wildlife activity, such as bears or moose near trails. This data is instantly displayed on the map for other users to view. Future work will include implementing this application on the AHS page, enriching data, designing additional tools and functionality, and developing a mobile application.

Flooding and its associated risks and challenges pose a persistent problem for the city of Houston, Texas. Worsened by climate change and increased urban growth, the growing flood severity appears to have far outpaced any current or past efforts towards managing floods. It is, therefore, imperative to understand how flooding can affect Houston residents, and who is the most at risk and the most vulnerable. While much has been written about flood risk in Houston, relatively little current research exists regarding flood vulnerability, which in this case can be described as the intersection of flood risk, shelter accessibility, and certain social justice factors. This study used principal component analysis (PCA) and dasymetric mapping to assess flood vulnerability in Harris County, which encompasses Houston. The goal of the project was to create a flood vulnerability index (FVI) that could be used to identify areas of high vulnerability. The results of the analysis identified several high-vulnerability areas around various watersheds in the county. Several of these areas have histories of flooding and slow recovery. These results indicated that the index could effectively identify areas of high vulnerability. The residents living in these areas would be likely to experience greater suffering during a flood than in other areas. The FVI could be used by disaster planners and managers to distribute resources and aid during a flood efficiently.

The Modifiable Areal Unit Problem (MAUP) is a phenomenon that occurs when data is arbitrarily aggregated or partitioned to spatial boundaries or units. This phenomenon occurs in most, if not all, spatial analysis efforts. The MAUP effects on analytical results cannot be predicted. The MAUP can cause analysis results, especially statistical results, to vary depending on the scale, aggregation, or partition used for analysis. This fact implies that inferences made based upon the results may not exist if the scale, aggregation unit, or partition change. Yet, in most spatial analysis efforts, there is no consideration of the MAUP. This study explored the MAUP scale effects on the results of optimized hot spot analysis and generalized linear regression analysis by using sociodemographic data and 2017 property crime incidents data in San Francisco, California. A comparative study was conducted at the census block group and census tract scales. The results suggested the presence of the MAUP in the statistical analyses. This thesis provides a framework for geospatial analysts to evaluate the MAUP. It also serves to highlight how the MAUP is present with commonly used analytical methods and data. The results of this research contribute to the body of literature regarding the MAUP effects on cluster and regression analysis.

The Democratic Republic of the Congo (DRC) is the fourth most populated country in Africa, with approximately 87 million people, of which 44 million are female. Unfortunately, it also has the 10th highest maternal mortality rate of any country in the world at 693 deaths per 100,000 births in 2015. High maternal mortality in the DRC is due in large part to pregnant mothers being remotely located from medical facilities and routinely dying from preventable complications. Cars are not prevalent in the DRC, and the most common means of travel is by foot due to the destruction of the infrastructure caused by the First and Second Congo Wars in the late 1990s. Walking long distances during pregnancy or while in labor and especially at night is a significant barrier for women seeking medical care. This study’s objective was to develop a simple methodology that could be used to identify ideal locations for new birth facilities where large populations are the furthest distance from existing facilities. The locations were identified through the generation of a tessellation grid over the areas of the DRC with low walking accessibility to medical facilities. For each tessellation grid cell, the distance to the nearest medical facility and the population within the cell were calculated. Based on a combination of population size and distance from a medical facility, a rank of locations for new facilities was created. Facilities built in these highest ranked locations would have the maximum impact by supporting the largest population that is the furthest distance from medical facilities. The resulting increase in medical accessibility could greatly decrease birth complications and preventable death

Ballot propositions have an important role shaping California’s laws. Through these propositions, California voters can directly influence changes to the state’s legislation. The importance of understanding ballot propositions has made them the subject of numerous studies. Examples of these studies include research about the influence of voter location, the influence of funding, and the impact of elected official support. Understanding election results can provide value to different groups and organizations; and while there are many different maps and visualizations that help users understand presidential election results, comprehensive data visualizations displaying ballot proposition results are difficult to locate. This thesis details the creation of a web Geographic Information Systems (GIS) application created to help users understand ballot propositions by visualizing results from recent statewide elections (2008- 2018). The application incorporates data from multiple sources and presents the data on a single platform in the form of an online dashboard. Users can search for propositions, view a detailed description of propositions, and visualize how different propositions performed across the state.

Climate change poses increasing risks to the world’s ecosystems and agricultural systems as greenhouse gas emissions are contributing to the unprecedented warming of the biosphere. One mechanism for capturing and storing carbon dioxide (CO2), a primary greenhouse gas, is the production and application of biochar, or carbonized biomass created in an oxygen-limited environment. The United Nations Intergovernmental Panel on Climate Change (IPCC) identifies biochar as stable organic carbon that can increase soil carbon sequestration, resilience, and fertility. Biochar researchers and enthusiasts have worked to identify scenarios that are conducive to the application of biochar and maximize its potential benefits. Researchers have addressed biochar feedstock, production technologies, physical and chemical properties, and biochar’s potential in energy generation, environmental remediation, resource management, land rehabilitation, and agricultural production. The Biochar for Agriculture Mapping Tool (BfAMT), which integrates Esri’s Collector for ArcGIS mobile application with a stand-alone web application developed with Esri’s Web App Builder (WAB), was designed to collect and display volunteered geographic information (VGI) about biochar agricultural sites on a global scale. With its editable feature services and map-driven forms, the BfAMT allows users to document their site-specific research and experimentation with biochar, thereby creating a geodatabase of biochar project locations, site attributes, and file attachments that facilitates research, coordination, and information sharing within the biochar community. Feedback from biochar users who beta-tested the BfAMT and completed an online survey questionnaire are presented and discussed. Recommended improvements offered by first-time users help guide the development and customization of the BfAMT as a workspace, spatial database, and promotional tool for local, regional, and global biochar activities.

Alternative fueled vehicles are positively shaping the environment, emissions, and social perceptions of vehicles. Several pieces of legislation and mandates have been passed in California to guide the state towards positive climate impact goals. Assembly Bill 118 and Executive Order B-16-2012 are notable pieces of legislation passed within the last decade that are driving California towards five million zero-emission vehicles by 2030. While these goals are aspirational, several state agencies have collaborated to create programs to accomplish this goal, including the Alternative and Renewable Fuel and Vehicle Technology Program. The California Energy Commission (CEC) has been tasked with implementing this program and releasing an annual Transportation Energy Demand Forecast. This report includes multiple charts and datasets, but no maps or visualizations to facilitate the public’s understanding as to the progress of said goals or the ability to achieve the interim 1.5 million zero-emission vehicles by the 2025 target. Research has shown that maps enable data to be better understood by both professionals and the general public. The primary goal of this thesis was to create a Web GIS Story Map that visualizes the progress towards meeting California’s alternatively fueled vehicle goals as a means of demonstrating the viability of Story Maps as a communication approach. The Story Map includes geographic representations of alternative fueled vehicles, spatial analysis of the demographic and economic adoptions throughout the state, and immersive multimedia to facilitate exploration of the alternative fueled vehicle program. This study evaluated the degree to which internal staff determined the Story Map useful versus approaches that are more traditional. Preliminary responses from internal staff showed that the Story Map was well organized and intuitive. This pilot project serves as a flagship Story Map that can be expanded upon and published by the CEC for the general public review in the near future.

The population decline of Western snowy plover (Charadrius nivosus nivosus) and subsequent listing as a threatened species by the U.S. Fish and Wildlife Service (USFWS) along the Pacific Coast, is a result of poor reproductive success that is considered directly related to habitat loss and nest predation. Habitat restoration and predator management are active key components to the recovery plan of this species. Usually “good faith” restoration plans are carried out often without the site-specific understanding of nest distribution and other factors that influence nesting to focus these efforts. Bottom-up factors, such as food availability, may contribute to the nest initiation by courting adult Western snowy plovers but these factors have not been directly assessed during the breeding season. Habitat varies between breeding sites; therefore, it is important to determine spatial patterns and regionally unique nest site selection for management actions. The goal of this study is to better inform management regarding where future habitat restoration or predator management activities need to be focused at Vandenberg Air Force Base. This thesis looked at the spatial and temporal changes in Western snowy plover nesting from 2002-2018 using Hot Spot Analysis to determine clustering of nest predation, initiation, and success throughout the breeding site. Specific areas of habitat were identified as significant hot spots in each nest category. These areas did not vary significantly year to year, however, analyzing 17 years together summarized hot spot trends overall which pinpoint significant areas where management actions should focus. Additionally, an exploratory analysis using habitat data on wrack abundance was used to identify possible spatial correlation between this habitat variable and nesting. The result of this analysis suggests no correlation between high wrack abundance and nesting, rather it indicates that low wrack abundance was more prevalent than high abundance during nest initiation.

Families create and build wealth through their homes. What happens when the traditional trajectory of home values is interrupted by external events, and how can homeowners determine appropriate compensation? During late 2015 and early 2016, a natural gas leak from an underground storage tank went unchecked in the community of Porter Ranch, California. Following the capping of the leak, the area was cleaned so that no visible sign of the leak remained. With over 11,000 people and 2,800 households affected, the question lingered of whether the homes suffered in value from the past gas leak. This study considers the limitation of the traditional methods for determining home value, and applied an alternative method borrowed from the social sciences with spatial context in the design. Propensity score matching was used to pair the census tracts in the Porter Ranch area with similarly structured census tracts within Los Angeles County. A regression analysis was used to identify key characteristics that would be used to identify similar census tracts using census demographic data. Difference-in-difference analysis and graphical trend analysis were then used to determine that a loss in value did exist in homes of the Porter Ranch community. The results of the study suggest that the home values in the Porter Ranch area increased at a slower pace than the comparable areas. This suggests that an environmental stigma exists in the Porter Ranch community, and the home values may have been negatively impacted by the past gas leak.

There are Complex Humanitarian Emergencies (CHE) happening worldwide. Currently, 68.5 million displaced persons exist around the world resulting from these CHEs. This project seeks to develop an Arcpy script for predicting movements within these crises to help government and non-government agencies manage resources. This project is intended to predict large-scale flows of people to allow agencies to prepare for an influx of displaced persons and organize shelters, food, clothing, medical personnel, and other necessities. ArcPy within ArcGIS Pro will give nontrained users an easy format to view spatially relevant processes and information about the CHE that they may be unable to share with the public. Additionally, this project looks at theoretical models of human movement that are most relevant to the circumstances of the CHE and should help speed communication between GIS and non-GIS professionals. This project uses the Syrian refugee crisis as a test case because of the large-scale movement and long duration involved.

According to social disorganization theory, crime is caused by social and economic variables at the neighborhood level. Coined in 1942 by Shaw and McKay, their research utilized the city of Chicago as a natural laboratory to examine how social and economic variables affected crime. It was decided to test this hypothesis using Pueblo, Colorado because of the high crime rate. To test if the theory of social disorganization applies to Pueblo, violent crime and socioeconomic status were analyzed spatially to answer the following questions: 1) have crime rates changed over time? 2) do the changes in crime rates have a spatial pattern? and 3) does the change in crime rates mirror housing values? Data on violent crimes was determined with the assistance of the Pueblo Police Department, who provided the location of 4,500 individual violent crimes across the city from 2006 to 2016. Statistical analysis showed that many of the counts of individual crime types were too low to be statistically significant, so the five crimes with the highest occurrence were used for further analysis. Socioeconomic status was determined using the housing values within the City of Pueblo. Hot spot analysis using the GI* statistic, which uses high and low z-scores to determine clusters of high values (hot spots) and clusters of low values (cold spots), was used to determine statistically significant high crime areas within Pueblo. These hot spots were used to determine where housing values would be analyzed. Statistical analysis showed that 2016 housing values sampled from within a hot spot area were lower than those samples from a non-hot spot area. Additionally, the average housing value in the sampled hot spot areas progressively decreased over the 10-year period, while those sampled in non-hot spots rebounded after the 2008 recession.

This work outlines an underwater laser scanner (ULS) operational readiness test (ORT) demonstrating the efficacy of ULS-200 in response to National Aeronautics and Space Administration (NASA) Planetary Science and Technology through Analog Research (PSTAR) Program knowledge gaps. This geographic information science and technology (GIST) project advises stakeholders on extravehicular activity (EVA) design and engineering (D&E) via cave diving. Analog surveys define strengths, weaknesses, opportunities, risks, and threats (SWORT) in three-dimensional (3D) remote sensing (RS) detection and ranging (DAR) via light (LiDAR) and photogrammetry (PhoDAR). Sidemount cave diving procedures and life-support systems (LSS) facilitate paleontological, hydrogeological, and microbiological evidence sampling, mitigating crew resource management (CRM) risks. 3D geographic information systems (GIS) toolkits produce LiDAR and PhoDAR digital terrain models (DTM) that require British Cave Research Association (BCRA) GIST quality assessment and control (QAC) modernization. Research outcomes included survey cost reductions, a < .15 cm precision ≈2,000m3 karst photoplethysmogram (volumetric LiDAR cavity system measurements) scan completed in.

Urban development is expanding in today's world, and the impacts on humans and the
environment are strained within these modern cityscapes. The Urban Heat Island phenomena and
habitat loss have raised concerns about the future of many the ecological health of many
metropolitan areas. Due to these concerns, cities have taken steps to reduce the negative impacts
on the urban environment with the use of green spaces. On the Island of Taiwan, the
municipality of Taipei is one metropolitan area that has experienced dramatic urban growth.
While multiple studies have investigated the avian diversity of Taipei's green spaces, most
studies have used traditional data collecting methods. These surveys are financially taxing and
time-consuming, which can limit the volume of recorded events. In this study, Volunteer
Generated Information (VGI) and Geospatial Information Systems (GIS) was used to determine
the biodiversity, richness, and species composition of 25 green spaces selected by data-driven
selection process within Taipei from 2016 to 2018. The eBird dataset and multiple indexes
served as indicators of ecological health allowed for monitoring of green spaces. This study
determined that there are relationships between biodiversity, richness and species composition of
green spaces within Taipei. However, specific site’s species compositions in VGI showed weak
links between the richness of the green space. VGI datasets and GIS could enable a cost-effective
way to monitor a city's green spaces effectively in the future.

Emergencies occur every day, and it is critical that emergency services respond to those emergencies to limit or prevent damage, injury, or loss of life. In order to provide effective service, it is critical that Fire and Emergency Medical Services (EMS) Departments understand where and how often emergencies occur so resources can be adequately distributed. In northeastern Illinois, QuadCom 911 dispatches emergency services for four fire departments and has been collecting emergency data for years. This study examines the spatial accessibility of QuadCom 911’s partner fire departments by using the two-step floating catchment area (2SFCA) method and to propose a standard range of acceptable accessibility values for suburban, regional emergency services. The method is used in three scenarios to explore how accessibility changes for QuadCom 911 if either of two fire stations were to close due to consolidation. Overall, the results show access to emergency services is affected by closing a fire station, but the effects are significantly higher if West Dundee Fire Department Station 2 (WDFD #2) is closed. Furthermore, the methodology proves that creating a standard range of acceptable accessibility values is possible, but this project did not have a large enough sample size for formal proposal of such a standard.

One of the greatest natural disaster threats to the Los Angeles area is earthquakes. A large earthquake could wreak havoc on the area, causing major damage and loss of life. Recent research from disaster events has shown that older adults (those 65 years and older) are one of the most vulnerable demographics to disasters. Older adults benefit from specialized planning and preparation designed specifically for their demographic. As the older adult populations are rapidly on the rise in the Los Angeles area, the county and city have devised plans to prepare and aid the community with natural disasters, including earthquakes. As a part of this effort, this project was to research, design, develop, and test a prototype web mapping application for the older adult population of Los Angeles to better prepare them for earthquakes and increase their awareness of nearby emergency services. This project brought together map layers of earthquakes hazards, emergency services, and shelters, and other mapping layers related to seniors such as senior centers and nursing homes into a single web mapping application. This project customized the web mapping application user interface and user experience design in order to make the application userfriendly to older adults. The developed web mapping application allowed users to zoom to their location, pan the map, identify map layers, turn on/off map layers, print a map, search for the nearest emergency services and shelters nearest their location, and add an emergency preparation needs point to the map. With the results of this project, progress was made with understanding successful and unsuccessful methods of apply UI/UX designs of web mapping applications for older adults. The results can be applied to future research in order to best meet the needs of older adults with web mapping applications.

Lead is a significant health threat to people, especially for children where elevated absorption of lead into the bloodstream can cause permanent damage. One site for concern of lead exposure is the surrounding communities of the retired Exide Technologies lead-acid battery smelter in Vernon, California. The California Department of Toxic Substances Control (DTSC) is leading an extensive cleanup effort to remove lead-contaminated soil from affected residences and eliminate the negative health risks posed by the contamination. Soil sampling conducted for approximately 8,500 parcels serves as the primary dataset for this research. While DTSC is currently undertaking the cleanup process on a parcel-by-parcel basis, this thesis works toward understanding the effect of geographic scale in the estimation of levels of lead contamination. It also offers alternatives for identifying priority areas for cleanup by using various aggregation methods and examining how the resulting values may be affected by scale. This research used Empirical Bayesian Kriging to produce interpolated surfaces of lead concentration values. Various aggregation methods were then utilized to aggregate the surfaces into easily defined geographical units of different scales, including block groups, blocks, and parcels. The resulting aggregation values include the mean, percent area, and a Hazard Quotient, an index value for determining health risk. The results demonstrate that the larger areas of the block groups moderate high lead concentration values and thus have lower overall aggregation values for the block groups. In contrast, blocks have a greater tendency to include these high lead concentrations in the aggregations resulting in higher overall values and wider ranges of values for the blocks. This research provides alternative approaches for prioritizing the cleanup of contaminated sites that could be more effective to address the health risks associated with contamination and can be applied to other areas faced with the same problem in the future.

Within the United States, wind energy has a steady growth rate, with an estimated installed capacity of 95-million mega-watts as of 2018. Despite the benefits associated with wind energy, there are negative impacts from wind energy facilities to avian species, ranging from collisions with site infrastructure and electrocution to habitat conversion. Golden eagles (Aquila chrysaetos) are one of the most studied species, in regards to wind energy expansion, due to their federally protected status and sensitivity to decreases in population numbers from anthropogenic sources. Studies have evaluated how golden eagles use their environments in order to better understand the conditions that result in increased fatality rates. This study evaluated the effect of prey distribution on non-adult golden eagles’ resource selection and used spatiotemporal pattern mining tools to evaluate patterns of habitat use at the Altamont Pass Wind Resource Area (Altamont). To evaluate the relationship between California ground squirrels (Otospermophilus beecheyi) and golden eagles, a species distribution model (SDM) was developed in Maxent for ground squirrels using burrow distribution as a proxy to estimate the ground squirrel distribution at the Altamont. The SDM output had good predictive capacity and was used in a resource selection function (RSF) model in R to evaluate if ground squirrel distribution affected resource selection of non-adult golden eagles. The resulting RSF model performed poorly, thus the influence of ground squirrel distribution on non-adult golden eagle resource selection remains largely unknown. Telemetry data was then analyzed using spatiotemporal hot spot analyses to identify patterns of use over space and time in ArcGIS Pro. Despite the inconclusive RSF model results, space-time pattern mining identified the hot spots of eagle activity within the Altamont, which could be a starting point for future analyses.

The Southern California Current System (SCCS) has been surveyed for 70 years by the research group CalCOFI, creating one of the longest running hydrographic datasets in the world. CalCOFI, California Cooperative Oceanic Fisheries Investigations, was initially created in 1949 to monitor sardine fisheries, but since then its mission has expanded to include the study of many more organisms and abiotic hydrographic attributes. The area consistently surveyed by CalCOFI stretches from Point Conception to San Diego and extends approximately 700 km offshore. It encompasses a small portion of the path traversed by the California Current (CC) as it travels southward along the North American west coast. In the SCCS, primary productivity is limited by nitrate availability. In previous decades, episodes of sea surface warming have been associated with a decrease in primary productivity due to a strengthening of the thermocline, which prevents nitrate from moving from the deep layer to the surface. This has led to the belief that increases in sea surface temperature (SST) due to global climate change will cause a decrease in primary production in the SCCS. However, recent research indicates that, despite an increase in SST, primary production is increasing in the SCCS, possibly because nitrate concentration in the deep water is increasing. This project seeks to shed further light on this unexpected trend and determine (1) is SST increasing in the CCS, and (2) is nitrate concentration increasing in the deep water of the CCS? A unique workflow was created using Empirical Bayesian Kriging 3D and the Space Time Cube toolset in ArcGIS Pro to analyze spatial and temporal patterns in temperature and nitrate in CalCOFI’s four-dimensional hydrographic dataset. Results show that SST and deep layer nitrate are increasing in parts of the CCS, particularly in the offshore portion of the study area. Comparison of spatial patterns to past research suggests increased nitrate may be delivered by the CC, perhaps due to global changes to circulation in the Pacific.

Elections are a pinnacle of modern democracy. Fair elections have been a key pillar to the success of representative democracy; yet, many of the age-old problems still exist. One of these problems is voter turnout and making voting accessible to every member of that democracy. In today’s elections, we are at an interesting crossroads of technology and convenience. California has recognized both trends and is implementing the Voter’s Choice Act (VCA) or SB 450 to guide the future of voting in the state by allowing people to vote over a multi-day period and at any location of their choosing. With this change, new voting locations will play a more critical role to the process than ever before. This paper discusses the design and implementation of a web GIS application that will assist Los Angeles County stakeholders and elections officials in choosing these new voting locations by providing tools for analysis. The tools developed are designed to assess potential site locations and their relationship to voters and businesses, voter age demographics, and voter turnout. The initial version of web GIS application documented here was reviewed by county officials. It will likely be provided to local stakeholders by the Los Angeles County Registrar-Recorder office and used in the first round of site-selection for these new voting locations.

Since the 1980s, obesity has been categorized as a national and global phenomenon. Although obesity rates in Minnesota have been consistently lower than the nation’s and neighboring states’ median, the rates have been gradually increasing. The disproportionateness of obesity rates between Minnesota, Minnesota’s neighboring states, and the United States suggest that aspects of the Minnesota environment are different. Potential explanatory variables included are linked to economic opportunity, demographics, healthy food availability, and health policies. Utilizing the methodology employed by Shresta et. al (2013), this study expanded it by incorporating more explanatory variables with the intention of building the best model to showcase the impact these variables have on obesity levels and disparities within the study area. Ordinary Least (OLS) and Exploratory Regression analyses were used to assess the spatial relationship between explanatory variables (socio-economics, socio-demographics, and healthy food accessibility) and the dependent variable (obesity levels) over space in Minnesota. The results suggested that the rate of obesity correlates weakly with diabetes, median family income, age, education, and healthy food availability at the county level. The analysis yielded an AICc = 402.068415 and AdjR2 = 0.231832 compared to hypothesis values of AICc = 410.857562 and AdjR2 = 0.162779. The explanatory variables included in the model did not have a strong relationship with the dependent variables in space. Given the relatively low correlations between the predicted relationships, the findings indicate that additional social, cultural, and behavioral factors are required to better explain the prevalence of obesity within Minnesota.

Perception of the surrounding environment influences personal behaviors and the way humans interact with each other over time. The fear of crime and perceptions of safety are a major contributor to these behaviors, and these perceptions influence the decisions made by law enforcement and city planners. Over time, a wide range of studies have been performed to understand the triggers that accentuate the fear of crime and the possible solutions to alleviate these fears. Most of these studies have been static in nature and rarely included a dynamic geospatial component. This thesis details the process used to integrate a dynamic geospatial component by developing a mobile Ecological Momentary Assessment (EMA) mobile GIS application prototype that: (1) enables users to collect spatiotemporal and fear of crime perception data in real time; (2) pushes notifications to users at specific times as a reminder to collect this data; (3) distributes this information to a Realtime database; and (4) provides values for integration into multiple GIS platforms for subsequent GIS analysis. Once the mobile application was ready for release, testers were distributed throughout the city of Albuquerque where they collected data and provided feedback on application functionality. At the conclusion of testing, all requirements to develop a functional EMA mobile application were achieved. Future work includes adding additional application features, external data sets for further analysis, and iPhone OS development for wider distribution.

Machine learning has emerged as a growing area of interest in species distribution modeling. Maxent is one machine learning tool that has gained wide use in such modeling. Maxent has shown good to superior performance compared to other SDM methods in studies using presenceonly species data when the tool is used properly. Often, however, due diligence with the selection of input data and model parameters is neglected, resulting in models of questionable quality. A range of factors need to be considered when setting up Maxent modeling. This study explored two of these. The performance impact of covariate scaling and the results of model tuning on Maxent species distribution models were examined, evaluating two questions related to these factors. Do higher resolution covariates yield a better performing Maxent model of potential habitat extent? Does a tuned Maxent model yield a better performing model of potential habitat than a model using the default Maxent settings? Two approaches to Maxent modeling, default parameters and tuned parameters, were used at two different covariate resolutions, yielding four evaluation models. Presence data for bristlecone pines (Pinus longaeva) provided the species example for the evaluation. Covariates were selected that are relevant to the species. These were scaled to match the two study resolutions. Model tuning was performed using the ENMeval R package. Quantitative and qualitative evaluations of the resulting models demonstrated improvements in the model performance in the tuned models. Results from the resolution aspects of the study were less conclusive. Issues with the quality of certain aspects of the climate and elevation data raised questions about the certainty of results at either resolution.

Defining place in health studies has been a crux for researchers as the definition of neighborhood is often regarded as adaptable to study needs and/or the preferences of the researcher. Health researchers commonly rely on measures of neighborhood that default to any number of predefined spatial administrative units, providing a relatively quick and cost-effective means to accessing and categorizing population data within a geographic area of interest. This approach to inferring population statistics assumes that median values for variables are relatively evenly disbursed across specific geographic areas of varying sizes. This thesis explores how research outcomes may be affected by the choice of geographic reporting zones. The primary research goal of this study was to compare geographic reporting zones within the State of Arizona and to determine how the choice of neighborhood would influence the resulting values for three commonly utilized social determinants of health; median household income, numbers of children and the elderly, and the percent Native American population. This study used administrative boundaries at the county, census tract, and census block group levels from the 2000 Decennial Census and examined if and what variation occurred within the resulting outcomes for differing reporting zones within the State of Arizona. The results of this thesis demonstrate that outcomes cannot be generalized across administrative units, that spatial aggregation will affect final outcomes, and that the choice of spatial reporting zone may produce widely different estimates for the same variable within a given geographic area. This thesis provides the foundation for future work investigating how choice of neighborhood can affect outcomes for small area studies and sets the framework for exploring what effects neighborhood definition might have on estimates of social determinants of health when proximity buffers are applied.

Primary care is crucial for both individual and public health outcomes. However, access to primary care remains insufficient for low-income populations even in developed countries like the United States. Striving to contribute to tackling this problem, this thesis provides a site suitability analysis for a new low-cost primary care facility in Service Planning Area 6 (SPA6) of Los Angeles County, a significantly under-served area that largely coincides with South Los Angeles. This thesis first employs fuzzy overlay analysis to evaluate candidate sites with a series of criteria including proximity to public transit, distance from existing low-income primary care facilities, appropriate zoning, an empty or under-utilized parcel, relatively cheap land cost, and relatively large low-income population. This thesis evaluates a short list of candidate sites resulting from the fuzzy overlay analysis by calculating their impacts on the primary care accessibility scores of each census tract in SPA6 using the 2-Step Floating Catchment Area method (2SFCA). The 2SFCA method quantitatively assesses primary care accessibility using floating catchment areas calculated based on travel time at population points and primary care provider points. To account for relatively low car-ownership rates among low-income populations, this thesis adopts a novel approach that defines the floating catchment area as the intersection of the two catchment areas: one defined by a 30-minute travel time via public transit and one defined by a 30-minute travel time via private vehicle. The final results from the fuzzy overlay and the 2SFCA analyses provide a list of suitable candidate sites with various geographical and physical attributes ranked by their accessibility scores, providing an informative, flexible, and intuitive guideline that caters to the different needs of potential decision makers looking for a site for a new low-cost primary care facility that would improve primary care accessibility in SPA6.

This project aims to increase knowledge of vegetation changes in arid and semi-arid areas in central Australia. Most of these zones are located across remote, sparsely-populated, large and geographically diverse regions, making them difficult to study (Burns et al., 2014). Satellite imagery and geographic information systems (GIS) are viable options to decrease the knowledge gap in time- and cost-effective ways and to understand how vegetation changes in areas with atypical annual seasons. The main goal of this thesis is to use modern techniques to understand vegetation dynamics occurring during 1989 – 1999 in Finke Gorge National Park (FGNP). During this time, land managers placed a fence around some park boundaries and removed a significant number of wild horses to enable the vulnerable vegetation to recover. An ensuing eight-year field study observed and documented changes. This thesis intends to do the same, using remote sensing (RS) and GIS techniques. A supervised classification of soils and plants is done using data collected during field surveys. Principal components analysis (PCA), a data reduction technique, is used on multitemporal images to enhance continuous spatial and temporal changes and to extract factors that can be attributed to land management efforts at FGNP. Visual interpretation of components and analysis of classification information allowed for exploration of vegetation dynamics at an appropriate spatial and temporal resolution to understand variation and trends across time. The resulting components are compared to results of previous field surveys conducted at the time. The principal components indicate there are natural and human-derived sources of variation. Rainfall and other environmental factors play a major role on vegetation recovery of areas inside the fence, however, components also indicate that other sources of variation, such as land management practices conducted in the area, are contributors to variation. The field survey results are comparable to the thesis results; however, modern technique use provides a different perspective of trends and variation.

This study predicts the spreading pattern of an invasive plant species, Spartium junceum, using Maxent, a type of Species Distribution Model (SDM). Species Distribution Modeling estimates the relationship between species records at sites within a given study area and the environmental or spatial characteristics of those sites. This study combines environmental variables found at sites where species occurrence has been confirmed and analyzes the results to predict future spreading patterns. A subset of occurrence data is used for quality control with the intended purpose of validating the accuracy of the model and its results. This study uses ArcGIS 10.6.1 and Maxent version 3.4.1 to perform presence-only species distribution modeling of Spartium junceum from data collected in 2011–2016 in the Angeles National Forest (ANF), which is managed by the U.S. Forest Service. The primary study area is the ANF with an emphasis on the San Gabriel Mountains which lie in the eastern portion of the national forest. The results will increase the exposure of Maxent as a feasible, cost-effective species distribution model that Federal land management agencies can incorporate into environmental analyses and environmental impact studies that contribute to their land management plans. The Angeles National Forest does not currently employ SDMs in its invasive species management plan and Maxent modeling represents an option for studying species distribution patterns where only presence data are available. Many studies use Maxent to understand species distributions of flora and fauna, but few such efforts are updated annually and used as a key indicator in a Federal Agency’s decision-making process in their land management plans and actions.

In Ohio, wind energy has been one of the fastest growing renewable energy sources. In 2003, Ohio’s wind farms produced 3.6 Mega-Watts (MW). Today the wind farms are producing over 600MW with an additional 2,000MW planned or being built. With this rapid increase in wind energy, comes many environmental concerns: habitat loss and fragmentation, noise and light pollution, spread of invasive species, and most concerning direct mortality to birds and bats. The Ohio Power Siting Board (OPSB) has full regulatory power for wind energy production in Ohio. In 2009, the OPSB asked the Ohio Department of Natural Resources, Division of Wildlife (DOW) to create an environmental plan to help regulate the environmental concerns. The DOW created the On-Shore Bird and Bat Pre- and Post- Construction Monitoring Protocol for Commercial Wind Energy Facilities in Ohio. Within this protocol the DOW created a landscape model to predict areas that were likely to be less impacted by wind development. Under the authority of the OPSB, the DOW is able to recommend environmental surveys to wind energy companies based on the relative predicted impact to the environment. However, this model was created over 10 years ago and with limited data. The purpose of this study was to recreate this model with updated knowledge and additional layers. This updated model was created with the hypothesis that landcover is the main driver for avian and bat species mortalities in Ohio, and areas with higher predicted risk will experience higher mortality than areas with lower predicted risk. A total of 6 habitat layers were used to predict relative risk to birds and bats from wind energy production. Using sensitivity analysis to derive specific weights for each layer, a best fit model was created. Species richness during the breeding season, was used to validate the model. The model predicted more than 30% of Ohio to be classified within the two highest risk levels to wind energy development.

With the possibility of mass movements such as landslides in the mountains near Richland, WA, understanding the detailed surface and subsurface geology is critical. These mountains, including Candy Mountain, Badger Mountain, and Little Badger Mountain, provide year-round public access to hiking trails offering stunning views of the Columbia River Basin located below. However, these views have also drawn housing developments onto the mountains. The new construction and increase in the water supply can contribute to landslide potential. Unfortunately, existing geologic maps of Candy Mountain, Badger Mountain, and Little Badger Mountain severely lack both quality and detail. This study establishes a greater understanding of the surface and subsurface geology of these mountains by developing updated professional-grade geologic maps, associated spatial database information, geologic cross-sections, and a surface geology 3D scene. Most notably, the identification of previously unmapped faults and folds provides users with a greater awareness of the geologic structures and landforms present in the project area. Improved geologic maps provide essential information necessary for engineers, geologists, and housing developers to explore potential landslide-prone areas and assess other information, such as hydrogeological conditions and surface/subsurface interactions.

Prior to Base Realignment and Closure (BRAC) 2005, military installations used Geographic Information Systems (GIS) in an ad-hoc capacity for a variety of installation management issues. BRAC, both a fiscal and political issue, required a common set of digital data and maps to visualize Department of Defense (DoD) installations in a GIS to support the real property–lifecycle process and associated decision-making central to the effort. This integration, however, began to provide business benefits in other installation management areas supported by policies such as the Paperwork Reduction Act of 1995, the Clinger-Cohen Act of 1996, and the E-Government Act of 2002. IGI&S programs have grown to provide geospatial data and tools for a variety of Installations and Environment (I&E) domains, including, but not limited to, planning, management, and operations, emergency response and recovery, environmental management, homeland defense, housing, recreation, and transportation. This research presents capacity building strategies and techniques to assist in both quantifying and qualifying IGI&S programs in the face of competing service- and installation-level priorities that often leads to defunding IGI&S programs. A lack of fiscal discipline in executing servicevalidated funding priorities such as IGI&S, in favor of local requirements puts life, health, and safety at risk as critical installation functions and services require IGI&S geospatial data to execute their mission. A withdrawal of investment in IGI&S also delays DoD strategic initiatives meant to improve installation functions and services, contributing to the decline of installations as projection platforms of military readiness and power during a time in which joint-force training and interoperability remains critical to US military superiority in the fight against emerging threats.

Throughout history, the United States has experienced waves of immigration from various nations, and the 170-year history of Mexican nationals migrating to the U.S. is well documented. Migration has had a major impact on the United States as immigrants and their decedents have contributed since the founding of the country, thus making the topic contentious. Further, understanding and measuring migration is complicated as it is not housed within one academic field. To help academics from various fields explore their questions about migration, this project developed a database allowing data exploration from the methods and tools of Geographic Information Science (GISci). Using GISci, this project created a geospatial database that can be and how they relate to sociodemographic data and other trends. The database could then reuse the data and update as the data becomes available. This was done through creating a non-relationship database diagram model. Relevant data was gathered from migration institutes and other sources into Excel spreadsheets before imported into ArcMap. Once the data was transferred into their appropriate attributes base on the diagram, thematic maps, and Structured Query Language (SQL) statements were tested to ensure that these features in ArcMap could be additions to the database. The GIS software ArcMap visualized the data spatially based on the research questions of the user. The database was tested and reviewed by five individuals who specialize in Mexican migration. Their feedback indicated that the database is worthy as an exploratory and collaborative tool and is appropriate in the fields of Anthropology, History, Geography, Mexican-American studies, and other academic fields.

As the global energy market pushes toward the further development and integration of renewable energy and reduced reliance on fossil fuels, the energy industry has looked to innovative solutions to solve the shortcomings of green energy production. Diurnal fluctuation in electrical production potential in solar and wind sources creates a need to develop ways to store surplus energy resources for later deployment. Pump storage hydroelectricity, in which surplus energy is used to pump water uphill to recharge a hydroelectric reservoir, holds a great deal of potential when used in conjunction with other types of renewable energy. This report documents the design and development of a two-phase analytical spatial model that identifies suitable locations for the placement of paired top and bottom terminal reservoirs of a modular pump storage hydroelectricity system (MPSHS). The first phase of the model applies user-defined search criteria to identify locations for the construction of terminal reservoirs that meet the relief and lateral run distance requirements. Further refinement of results from the first modeling phase using secondary information can be used to rank suitable locations based on user-supplied environmental, economic, and socio-demographic constraints and preferences. This thesis presents details of model function as well as case study results for Los Angeles County.

The future of the automotive industry continues to head towards the development of autonomous vehicles. Without a human driver behind the wheel, the self-driving vehicle must be able to navigate itself within the road network. This research project investigates the application of aerial drones, also known as unmanned aerial vehicles (UAVs), as an alternative data collection method to create HD datasets for use in autonomous vehicles. Drones may be a low-cost alternative method to the current leading data collection method of sensor-equipped mappingvehicles. A Phantom 4 drone was used in two case studies to create orthomosaics of parking lots. The drone-generated orthomosaics were processed by methods of manual delineation and toolbased extraction to evaluate different methods of processing high-resolution data. In addition, current HD data standards were acquired from various sources to evaluate the results of the research project and to compare data collection methods. The results show that drone-based data collection with GPS correction techniques can be an accurate and low-cost alternative method. Both manual delineation and tool-based extraction techniques proved successful in extracting desired feature classes from the high-resolution imagery.

As of April 19th, 2018, there were 34,771 verified locations of cogongrass (Imperata cylindrica (L.) Beauv.) infestations within the state of Alabama. Cogongrass is a highly invasive non-native species of rhizomatous grass that is considered one of the ten worst weeds worldwide. This highly invasive and environmentally destructive species has caused significant damage throughout its current distribution and efforts to control and eradicate the threat have been underway for almost a decade. This study utilized the Maximum Entropy (Maxent) model to predict the location of invasive cogongrass within the state of Alabama. The model developed using the presence locations and environmental data for the Model Study Area, one Alabama Forest Commission (AFC) Work Unit, was applied to two additional AFC Work Units to test transferability of the model to areas of similar and dissimilar ecological and geographic makeup. The Model Study Area’s Maxent model resulted in an acceptable AUC (0.725 with sd = 0.0010) and fair TSS score (0.4087) with a test omission rate of 0.0832. Transferability test results differed between the two test areas. Using the Model Study Area’s model on Test Area 1, an area similar in most aspects to the Model Study Area, resulted in an AUC of 0.746 with a standard deviation of 0.002, a TSS score of 0.3944 and a test omission rate of 0.0807. These results indicated that the original model was sufficiently transferable to the similar Test Area 1. Test Area 2 was dissimilar from the Model Study Area in most environmental covariates as well as number of verified presence point locations. Applying the model to Test Area 2 resulted in an AUC of 0.846 with a standard deviation of 0.017, a TSS score of 0.2377 and a test omission rate of 0.2941. These results suggest the need for some concern about the suitability of the transferred model to Test Area 2.

Human activity has always impacted wildlife and the environment, fragmenting and reducing habitats on a global scale. The jaguar (Panthera onca) once extended from Argentina to the American Southwest. After being hunted to extinction in the United States 60 years ago and with 54% of its habitat reduced, the jaguar is at its highest risk of extinction historically. However, jaguars have finally started dispersing back into Southern Arizona and New Mexico. Jaguars are known to disperse to set up new territories or reclaim lost habitat; males have been observed to disperse hundreds of kilometers from their original territory. In order to make sure that jaguars have a way to grow in population and expand their territory into the United States, there must be a path for them to enter the United States from Mexico and into new habitable territory. However, existing and future physical structures along the United States-Mexican border affect their path. In this study, multiple border structure expansion scenarios were investigated to determine the change in cost to jaguar dispersal paths crossing the border into habitable areas of the United States. Evaluating change in cost refers to the change in difficulty of terrain, distance, and other factors that affect the jaguar’s ease of movement. Paths across the border were analyzed and the most sensitive locations to jaguar dispersal cost were identified. These paths were investigated using least-cost path analysis. As the border structures expanded under each scenario and the ability to move across the border diminished for the jaguars, the cost of travel increased. The increase in cost was minimal and gradual at first, then dramatically increased when the paths into the United States eventually closed. The most sensitive locations to border structures for jaguar dispersal were identified to provide the proper jurisdictions with information on where to either implement wildlife corridors that create a safe path across the border or avoid building future border structures in those areas.

Analyzing pelagic shark behavior is an ongoing challenge due to the highly migratory nature of these animals, as well as outside threats such as overfishing and climate change. Increased protection of vital habitats is essential in combating declining species numbers. Although some shark species, like the shortfin mako (Isurus oxyrinchus), have made a steady comeback in the last decade, there is still significant room for improvement. Comprehending the connection between how sharks use their environment and move between protected territories can benefit our understanding of shark behavior and conservation as a whole. By analyzing shark movements over time and creating visual representations of core habitat use areas, an assessment can be made on the potential for implementation of seasonal dynamic marine protected areas (DMPAs) in New Zealand’s waters to aid in pelagic conservation. Starting with a large spatio-temporal dataset of tagging data collected for 13 mako sharks over five years, these data points were first cleaned and filtered in order to create individual shark track lines for visualization of the data as a whole. Next each shark’s track was divided into seasonal chunks and these were buffered to a 32km wide zone, which, based on the data, accounts for an average day’s movement of a mako shark. This collection of seasonally tagged polygons represent the areas used by each shark in each season. The next step was to intersect and count overlapping seasonal polygons to identify the “high use” areas. The result is a map showing areas where seasonal closures might benefit overall conservation, the areas to consider as the core for future DMPAs.

Natural disaster events such as tropical storms and earthquakes have gained widespread attention from the general public. While pictures provided by the media may tell a convincing story, data, statistics, and maps provide the foundation for a more empirical approach to trend analysis. This web GIS application provides users the ability to explore earthquake and tropical storm events over the last 30 years and analyze trends in frequency and intensity of the events. The web application consists of time-enabled maps and charts displaying global statistics in earthquake and tropical storm frequency and intensity over the last 30 years. It is designed for members of the general public who have a working knowledge of earthquake and hurricane science and an interest in exploring whether there or not there are increasing trends in severe earthquake and tropical storm events.

Disruptions like accidents or closures on metropolitan freeways have the potential to increase traffic congestion on surface streets. Through spatiotemporal analysis, this project evaluates associations between traffic congestion spikes on arterial streets with freeway incidents. The unexpected increase of traffic on city streets from freeway overflow was expected to not only create severe gridlock negating the expected benefit for the motorist avoiding freeway delays, but also cause undue stress for local traffic normally on those streets. This thesis takes the initial steps in spatiotemporal analysis to assess how strong the associations are between incidents on the freeway and increased arterial traffic. Data preparation models from Alteryx are used in ESRI’s ArcGIS Pro to provide a contextually rich multi-dimensional representation of sensor location, time and traffic speeds near freeway incident locations. This enables an intuitive way to recognize potential associations between speed data collection points. The use cases analyzed by this study were predicated on a long-duration traffic accident for which medical services were required. The results show that almost no clear association can be made for incidents of this magnitude. Using data about these effects and more severe use cases like complete freeway closures in concert with the visualization techniques presented, additional studies can be built to support determination of whether or not more significant disruptions may have clear associations. From that point, mitigation options can be designed to reroute traffic through techniques like optimizing traffic lights and active traffic rerouting.

In order to increase citizen engagement, in 2013, the City of Los Angeles introduced the MyLA311 application, a smartphone app that allows residents to easily request city services. Previous service requests were funneled through four separate data service management systems and lacked transparency; the improved centralized system increases public data access and efficiency, all while ensuring a uniform tracking methodology across all departments. Citizens act as agents creating data each time a service request is made. Ease of reporting and increased use of mobile applications or digital platforms to track and monitor service requests creates huge volumes of volunteered geographic information (VGI) data. Los Angeles’s shift towards open data supports data-driven decision-making regarding city services and mitigation of problems. While the original purpose behind the push towards publicly accessible information was accountability, a new purpose for the data was found in the possibility of constituents creating additional insights. The attributes of VGI provided through the MyLA311 service requests were analyzed to determine fitness for use in spatial analysis. Los Angeles experiences a great deal of spatial heterogeneity given the differences in socioeconomic attributes and local neighborhood contexts. Distinct signatures of the local urban context, similar to a neighborhood, are determined through a multivariate cluster analysis of MyLA311 Service Requests and sociodemographic data at the census tract level. This spatial analysis provides stakeholders and civic leaders with insights into which physical problems need focus in certain geographically defined communities detailed in the results and conclusion.

Geographic information systems (GIS) professionals have an impressive and powerful array of software tools and services at their disposal, yet Web GIS applications do not consistently meet the expectations of end-user business requirements. This thesis examines an integrated User-Centered Agile Software Development (UCASD) framework, as a vehicle for Web GIS application developers to deliver solutions that meet end-user requirements. Methods employed for this research include consultation of both academic and business literature, case studies, the design of a UCASD, and the creation of a web application to test the implementation of the UCASD framework. The goal of this thesis is to create an integrated UCASD framework for Web GIS design and development that is based on the adaptation of existing Agile-based methodologies such as Scrum and User Stories. The framework includes GIS-specific design considerations, an extended planning iteration, and an additional testing period to ensure that the application satisfies user specifications. The framework is tested through the development of a GIS web application, the Property Information Application (PIA) for Snohomish County. The PIA is an educational tool that provides permitting and property development explanations to citizens in regards to what they can do with their properties. Implementing the UCASD by means of testing the proposed Web GIS application proved to render a better product tailored to the specifications of end users.

An integrated ocean water quality geodatabase for the West Hawai‘i Island region is of interest to local scientists who want to assess near-shore ocean waters because the unique properties of this environment allow the data to speak for the environment as a whole. With guidance from local environmental scientists, disparate near-shore water quality projects from different organizations were combined and integrated into a single geodatabase using reproducible methods. This study used SQL query methods to extract data from regulatory monitoring and scholarly research documents into a professional spatio-temporal water science database for use in the Esri ArcGIS Pro software program. The final geodatabase contains 100,000 analyte results from 15,000 samples at 300 stations. The geodatabase, data, methods, images and data and documentation sources used to produce these results were published at GitHub free for use. This geodatabase provides a GIS foundation to support the future development of online web maps and story maps that can be used to inform the public about ocean water quality changes.

This project aims to increase knowledge of vegetation changes in arid and semi-arid areas in central Australia. Most of these zones are located across remote, sparsely-populated, large and geographically diverse regions, making them difficult to study (Burns et al., 2014). Satellite imagery and geographic information systems (GIS) are viable options to decrease the knowledge gap in time- and cost-effective ways and to understand how vegetation changes in areas with atypical annual seasons. The main goal of this thesis is to use modern techniques to understand vegetation dynamics occurring during 1989 - 1999 in Finke Gorge National Park (FGNP). During this time, land managers placed a fence around some park boundaries and removed a significant number of wild horses to enable the vulnerable vegetation to recover. An ensuing eight-year field study observed and documented changes. This thesis intends to do the same, using remote sensing (RS) and GIS techniques. A supervised classification of soils and plants is done using data collected during field surveys. Principal components analysis (PCA), a data reduction technique, is used on multitemporal images to enhance continuous spatial and temporal changes and to extract factors that can be attributed to land management efforts at FGNP. Visual interpretation of components and analysis of classification information allowed for exploration of vegetation dynamics at an appropriate spatial and temporal resolution to understand variation and trends across time. The resulting components are compared to results of previous field surveys conducted at the time. The principal components indicate there are natural and human-derived sources of variation. Rainfall and other environmental factors play a major role on vegetation recovery of areas inside the fence, however, components also indicate that other sources of variation, such as land management practices conducted in the area, are contributors to variation. The field survey results are comparable to the thesis results; however, modern technique use provides a different perspective of trends and variation.

Education and GIS have both moved onto mobile platforms to take advantage of the flexibility of having a device wherever the learner or user is. This project brings these innovations onto mobile platforms together with the creation of a mobile framework for GIS education. The framework demonstrates the ability to incorporate a wide variety of GIS as possible components within an educational lesson on GIS&T principles and workflows. This project looks at the research behind GIS&T pedagogy, M-learning with GIS&T, and GIS&T as a tool to enable study. The design and considerations for the project are explained and the authoring and lesson delivery applications built for this project are described. The project's authoring application and its companion lesson delivery application provide a proof-of-concept for GIS learning on the go. The results show that some objectives within the GIS&T Body of Knowledge are well suited for this framework, while some objectives cannot be met using this framework alone. Additional opportunities for development of the framework and research with the existing framework are also presented.

When project proponents wish to assess a development site for jurisdictional wetland impacts, they are traditionally left with two options: a wetland determination or a delineation. A wetland determination is customarily a desktop assessment of the site including, but not limited to, the following datasets: National Wetland Inventory (NWI), National Hydrography Dataset (NHD), National Resources Conservation Services Soil Survey (NRCS), topographic maps and satellite imagery. A wetland delineation assesses the presence of hydrophytic vegetation, hydric soils and hydrology during field evaluation. The NWI is typically used to determine where existing wetlands are in order to determine if they qualify as jurisdictional wetlands. This allows project proponents to either take the appropriate avoidance measures to reduce impacts to the wetland or determine if a full wetland delineation is required to apply for a Section 404 permit. In some cases, NWI maps have not been updated for up to 30 years, and these mapped wetlands are limited by conditions that were present at the time the aerial imagery was taken. This thesis shows that by incorporating unmanned aerial systems (UAS) into a wetlands determination, wetland specialists and project planners can capture current conditions of the development site (i.e. topography, disturbance, land cover, etc.) within efficient time frames and assess the potential extent of a wetland(s). This allows project proponents to avoid the cost and time restrictions that come from a full wetland delineation. The UAS imagery was compared to historically mapped wetlands still present; UAS improved placement of wetlands on the landscape and had on average a 76.5% overlap with delineated wetlands. Future research into buffer distances, topography, seasonality and thermal imagery could improve this overlap. With the aerial imagery from current conditions, wetland specialists can assess potential wetland extent, hydrology, highwater marks, and coarsely classify vegetative condition.

A United States Geological Survey (USGS) researcher has been studying impacts of climate change on American Pika (Ochotona princeps) from the mid 1990's through 2017. This project aims to contribute to research on the American Pika by building a geodatabase to store and provide access to data on pika populations throughout the Great Basin region of the Western United States. The geodatabase contains pika presence and absence data for locations of talus, which includes habitat areas that have been previously surveyed or may be potentially surveyed in the future. The project used formatted data provided from field surveyed talus that have been digitized on www.caltopo.com, digitized new talus that have been more recently surveyed, and imported GPS points for presence/absence captured in Excel spreadsheets. The end result of this project was a geodatabase that housed presence/absence points, talus polygons, site locations, temperature sensor locations, and temperature/relative humidity data. Several queries were completed that show proper importation and relationships of all data. Working closely with project researchers, this study allows for database expansion as needed for future research needs. Studying presence/absence of American Pika allows for further understanding of climatic impacts in niche habitats that are especially susceptible to environmental change. This project also provides the opportunity for improved analysis and long term data storage relating to these presence/absence locations throughout the Great Basin region. The end result supports expansion of the database structure for future field seasons and data inclusion.

Over time, the methods and technologies by which we produce and harvest our food have advanced. Large corporations are quick to adopt new technologies and processes, but smaller farms can struggle to see the value in pursuing advanced technologies for farm management. Development of a streamlined protocol for introducing geospatial technology at the individual farm level can help prioritize operations, and help develop long-term operational plans. While the benefits of integrating GIS software and tools are apparent to corporate farm managers, or agricultural economists, they are not always as apparent or easily accessible to small farmers. Using the research farm in Freeville as a case study, a developmental framework for other farmers at Cornell University and around Tompkins County for small-scale geospatial data integration will develop. With a focus on easy-to-obtain datasets, the procedures outlined on this paper will articulate in a way that non-geospatial data users can understand and build upon. This paper will examine the possible benefits of implementing GIS technology in small farming communities of like that of the Homer C. Thompson Research Farm and discuss how it can improve the visualization and management of small farms. While the overall impact of introducing geospatial technology at the small farm level is not quantifiable in this paper, understanding what data is available, and its impact on farm operations, can be beneficial in the long-term planning and management of small farms.

The state of Florida has access to vast marine life resources. The Florida marine commercial fishery plays a significant part in the state economy. Hundreds of thousands of commercial fishing trips occur each year totaling hundreds of millions of dollars in dockside value. Because of the weight of the commercial fishery in the state's natural resources, sustainable management is critical. In 1976, the Magnuson-Stevens Marine Fisheries Management and Conservation Act outlined a federal plan for the management of the United States marine resources. The Gulf of Mexico Fisheries Management Council worked with Florida to develop the Florida Marine Fisheries Trip Ticket Program (TTP), to fulfill the mandate to record all licensed commercial landings in the state. TTP landings summaries are available to the public via a web application hosted by the Florida Fish and Wildlife Conservation Commission. These summaries contain a wealth of data in tabular format. This thesis aims to create a web GIS (The Landings Explorer) of the landings summaries data, to increase accessibility and provide spatial and temporal insight to fishery stakeholders and the general public. By creating widgets and user controls in the ASP.NET Landings Explorer and the Esri JS API v4.x, a user is able to select any species and year from the landings summaries data and render a web map with classes derived from each of the data attributes within these summaries. The application strives to provide fishermen, seafood processors, restaurateurs, and other stakeholders with insights about the commercial fishery to aid in economic and conservatory decision making. By developing the Landings Explorer with the responsive web frameworks, Calcite Maps and Bootstrap, the application seeks to allow users to access this data on any internet connected device from dockside to the kitchen.

The San Andreas Faultline is the largest fault in California. On average, this fault has produced a major earthquake every 150 years, the last to strike the southern section was the magnitude 7.9 Fort Tejon earthquake of 1857. Today the Greater LA Area is one of the largest urban agglomerations in the world and the second largest metropolitan region in the U.S. The area is well known for its urban sprawl and expansive highway system. The weak points of any highway system are the bridges and overpasses. Modeling the effects of an earthquake on this infrastructure will help inform emergency planning and speed economic recovery. Experts have predicted a major earthquake, from magnitude 7.0 to 8.0, will strike the fault within the next 30 years. The goal of this project was to examine enhanced HAZUS hazard datasets to assess potential earthquake damage to highway bridges and how this may correspond to bridge closures. NEHRP soils maps were created by joining shear wave velocity data to STATSGO and Geological Unit data, then classifying each soil unit by the NEHRP class shear wave velocity range. USGS Scenario ShakeMaps at M7.4 and M8.0 were selected near the area of fault section with the highest probability of a great earthquake. Eight scenarios were modeled with this data. Results of this work show that user-supplied datasets for ground motion generally reduce HAZUS bridge damage outputs, that all earthquake scenarios will significantly damage southern California bridge infrastructure, and how relative damage state outputs translate into bridge restrictions and closures.

There were around 65,000 search and rescue (SAR) incidents from 1992 to 2009 throughout national parks in the United States. Of those incidents, around 2,500 were fatal. Studies surrounding SAR incident data typically revolve around the subject rescue and recovery process. The study of lost person behavior and psychology can also affect this field of work in a way that is beneficial to the lost subject. Search and rescue incident commanders (IC) must exhaust all possible indications of where the subject may be and which direction they may have traveled. The objective of this study was to apply least cost path analysis to search and rescue data in Yosemite National Park. For this study to be successful, the cost paths will indicate possible evidence of deviation from designated park trails. The least cost path analysis required an incident planning point (IPP) and a subject found or recovery point for each case investigated. An overland travel cost surface was constructed using impedance tables from Integrated Geospatial Tools for Search and Rescue (IGT4SAR). One hundred seventeen SAR cases were subject to least cost path analysis in this study. Resulting paths were traced manually from beginning to end to find points of divergence from trails or roads. Thirty-six paths contained likely divergence points. Thirty-one were from trails and five were from roads. This confirmed the least cost path analysis and trail divergence studies were successful. There were also clusters of divergence points in some park locations, suggesting possible problematic areas. While it is not implied that the paths are exactly those chosen by lost individuals, the methodology can be reproduced with different data to assist with park trail construction or maintenance.

Across the nation wildfires in national forests and parks annually affect millions of acres of public lands, destroying recreational sites, historical areas, and scenic wilderness, and costing taxpayers hundreds of millions of dollars every year in suppression costs and lost resources. This research examined the spatial correlation between human activities and human caused wildfire occurrences within the Willamette National Forest to explore whether these activities might be responsible for many wildfire ignitions. Between 1995 to 2008, 493 human caused fires occurred. The density of these fires was investigated to identify clustering near recreational sites and human infrastructures. Maxent was used to model the probability of wildfire occurrences in relation to the recreational sites and human infrastructure areas located throughout the Forest. It was initially hypothesized that more wildfires occur near specific kinds of recreational sites than elsewhere. Preliminary data exploration showed high densities of wildfire occurrences near the towns, human infrastructures, and major highways although these were also areas of clusters of recreational sites. Thus, it was not possible to identify visually which particular activities were most strongly related to wildfire ignitions. Maxent results revealed that areas of high population densities and recreation site clusters were more likely to correspond to areas of more human caused wildfire ignitions.

Colorado is a popular destination for elk hunters. Despite ample opportunities, success rates for elk hunters in Colorado are often low - the combined success rate for all 2016 Colorado elk hunting seasons was only 18 percent. Many variables seem likely to have an impact on hunter success; one possibility is terrain ruggedness. The main research question of this study is whether more rugged topography is correlated with hunter success rates. Such a finding could benefit hunters by showing which areas have higher harvest success rates. Furthermore, this study could benefit wildlife management communities by illustrating which areas need an increase or decrease in hunting licenses in addition to changes in season structure. Since location of elk harvests are not consistently mapped, regression analysis was utilized to explain spatial patterns. Using ArcMap, this study examines the correlation between terrain ruggedness and hunter success for the 93 Game Management Units (GMU) that offer over-the-counter (OTC) second and third rifle season hunting licenses. The 2012 to 2016 seasons were analyzed in order to account for variation in weather patterns and differences in the number of hunting licenses issued. Average annual GMU success rate was the dependent variable while average elk density, terrain ruggedness, average hunter density, percent of public land, and road density were the exploratory variables. Terrain ruggedness was not a significant variable. Average elk density and public land percentage were the only two significant variables. Future studies should analyze each year separately, analyze public land hunters that hunted OTC rifle seasons, and consider weather variables.

Park accessibility is important for city planners because the accessibility of parks can impact people throughout the community. Youth park accessibility is especially important, as parks positively impact physical, emotional, and social development. This study uses dasymetric mapping of census block group population data to estimate segments of youth population at each residential parcel, and then associates those segments with age-appropriate active play features at each park. Network analysis connects parcels to parks and their amenities, providing a more precise accessibility rating at the city-level than studies based solely on geodesic buffers from park centroids. This study shows that while Alexandria, Virginia has many parks throughout the city, the distribution of age-appropriate active play features is not uniform. Most children in Alexandria have access to at least one active-play park. Only 132 parcels have zero access to ageappropriate, active-play parks, a rate of less than one-hundredth of a percent. There are areas for improvement, but the City of Alexandria has done an excellent job ensuring children have access to active play parks. For other cities, this sort of accessibility analysis could help planners to target areas to increase funding for fitness amenities and programs within parks, establish new parks, or add pedestrian paths to improve walkability to existing park resources.

This project employs a mix of archival and digital techniques to evaluate an extensive geographic survey of the St. Lawrence River valley, conducted under auspices of the Quebec Governor James Murray following the 1760 British conquest of Canada. I show how scholars have misread the historical evidence surrounding the production and dissemination of this survey by ignoring the patronage motives of those involved in its production. I also use archival evidence to examine contemporary practices and ideas surrounding maps and accuracy. I discuss how I built a Historical Geographic Information System by manually creating and spatially adjusting vector data from Murray's personal copy of the survey (which was composed of fortyfour individual map sheets measuring forty-five by thirty-six feet when fully assembled). This dataset allowed me to establish that the survey demonstrated a high degree of spatial accuracy for the eighteenth century. Here I also discuss methods for retroactively creating administrative boundaries, which were undocumented in the period. This allowed me to create spatial interoperability with other contemporary quantitative records?historical censuses and parish registers, which recorded births, marriages, and deaths?to evaluate the reliability of these various administrative technologies of state during the early modern period. I conclude that while the historical censuses undercounted people, the survey and parish registers support each other's conclusions, which suggest their demographic accuracy. This work serves as a proof of concept for a much larger spatial humanities project that would employ these same techniques to digitally process the series of other geographic surveys conducted throughout British North America between 1765 and 1777 to capture a geographic snapshot of the late colonial period

Understanding spatial and temporal change in distribution of endangered species within urban, fragmented landscapes has increased as an area of ecological study in the last fifty years in concert with improvement of environmental protection regulations. This research involves designing a species distribution model for Pacific pocket mouse (Perognathus longimembrus pacificus; PPM) to generate predictions about their habitat use. The main goal was to understand the relationship between distinct occurrence locations and environmental variables within a 0.12- km2 Habitat Conservation Area in May 2009 for later spatio-temporal comparison. Environmental variable layers were generated using supervised classification of Digital Globe's WorldView-2 high-resolution satellite imagery, in addition to other vegetation health measures and topography. A model was developed using the open source software program Maxent to spatially represent the distribution of PPM and the variables that may have influenced their presence. Results indicated that distance to houses and anthropogenic infrastructure strongly influences PPM distribution. Proximity to California sagebrush (Artemisia californica) and buckwheat (Eriogonum fasciculatum) show a positive relationship with PPM occurrence. Another strong positive influence on PPM presence was proximity to a recreational trail, which indicates that a level of moderate disturbance may benefit the species. This thesis presents the idea that appropriate habitat disturbance may be necessary to improve the spatial distribution of the PPM, and suggests ideas for further research to enhance understanding of human and environmental impacts to the species.

Location-based data is becoming more and more integrated into our society from internal navigation to food delivery services. Even the collection of positional data once only collected by professionals with survey equipment is now achievable by anyone with a smartphone. Several studies have looked at the positional accuracy of different smartphones and found that they are not as accurate as dedicated GPS receivers are. Previous research has also shown that positional accuracy in smartphones changes when exposed to adverse conditions like building shadows, tree cover, and canyons. The aim of this study was to see if the use of a Broadband Global Area Network (BGAN) terminal could consistently improve the positional accuracy of a smartphone, and if that improvement was consistent when exposed to adverse conditions. An experiment was designed and used to test the pairing of these devices using NGS benchmarks and historical landmarks as control points. Findings show that the use of a BGAN terminal does influence the positional locations of the smartphone but not in a consistent manner. At some sites, the smartphone improved in its positional accuracy when the BGAN signal was introduced but at others, there was a decrease in positional accuracy. These mixed results lead to no definitive conclusions reached beyond recommendations for future testing.

Thousands of people around the world lose contact with family members each year due to armed conflict, disaster, and migration. The International Committee of the Red Cross provides coordination between National Red Cross and Red Crescent Societies around the world to restore contact or provide closure for those with missing loved ones. The American Red Cross plays a part in this effort by collecting information to initiate a trace request on behalf of someone within the US for a loved one missing abroad and by conducting a trace for missing persons within the US on behalf of family members in other countries. This thesis describes the creation of a web app, referred to as the American Red Cross International Services Outreach web app (the "ISO App"), to support this work. Community outreach is vital to educating immigrants about the scope and availability of Red Cross services. The ability to locate specific communities allows workers to target outreach efforts, saving valuable time and volunteer resources; the ISO App helps American Red Cross International Services staff locate specific immigrant communities anywhere in the United States. The ISO App is centered on an interactive map of 145 American Community Survey (ACS) 'place-of-birth for the foreign-born population' fields at tract level. The ISO App does not provide predictions or conclusions about the location of individuals or communities, but provides geospatial clues that may increase chance of a successful trace or outreach effort. To meet American Red Cross technical requirements, the ISO App is built within an out-of-the-box ArcGIS Story Map template, compatible with ArcGIS Enterprise 10.4 Portal installation. This project will serve as a model for additional web apps utilizing ACS data to support other American Red Cross lines of service, as well as other organizations.

North Kohala, the northwestern district on the island of Hawai`i, does not have its own local government as is common in other states in the union. In the State of Hawai`i, the lowest level of local government is County, of which there are five, each made up of one or more islands. Thus, community development and other policies in North Kohala are governed by its Community Development Plan, which outlines and provides guidance for development within this district, overseen by the County of Hawai`i. The community of North Kohala desires to develop guidelines and policies that will augment the vitality of arts and culture facilities and activities within their district. A non-profit organization focused on sustainability and community building, OneIsland, has been funded to assist by garnering community support through involvement in the planning process. OneIsland intends to use Geographic Information Systems (GIS) and associated technologies such as Esri’s ArcGIS Online (AGOL); however, as an organization, they focus on sustainability and community building and do not have technical support. As such, they needed assistance to create a web-based mapping tool which can be used to facilitate communication during community involvement sessions called Listening Circles (LCs).
This thesis focused on creating a web map application that OneIsland could use to collect community input regarding the locations of future arts and cultural events and establishments in the district. The application needed to be simple non-web programmers and general public internet users to operate and manage while incorporating local artistry in the form of icons and a splash screen map. A workflow was generated for OneIsland to follow until they had become familiar with the platform used. Use tests with OneIsland and two unrelated naïve users indicated that the web map application and supporting workflow were simple to use and follow while still allowing OneIsland to collect community input.

The La Brea Tar Pits and Museum in the middle of Los Angeles, California is a paleontological marvel containing numerous fossil-rich asphalt deposits. Until very recently, the museum only recorded their findings in non-spatial databases. As a continuation of work completed by a former USC Geographic Information Science and Technology student to develop spatial databases documenting artifacts for the museum, the main objective of this thesis project was to create a methodology for visualizing fossils as high-resolution 3D objects on a 3D map in their pre-excavation, in-situ locations. Museum scientists selected nineteen fossils from one asphalt deposit for mapping. The fossils were laser scanned by museum scientists, and the resulting 3D objects were provided for this project with accompanying locality data gathered in the manner of a traditional paleontological dig. The data required extensive processing prior to importing the 3D objects into a GIS, including image file conversion, location and orientation diagramming and steps for coordinate transformation from paleontological location measurements to realworld, geographic coordinates. The 3D objects were then imported and manually positioned in a 3D GIS map beneath the earth’s surface. The resulting 3D model provides an interactive, GISenabled visualization of the nineteen fossils in their original locations and orientations prior to excavation. It is intended that this project support future research efforts of the museum scientists in spatial analysis and modeling of fossils and substrate (tar pits), ultimately to improve our understanding of Ice Age animals and the environments in which they lived and died. In addition, the results of this project serve as an example application of 3D GIS capabilities that can support forensic archaeology, an important tool in intelligence and criminal investigations. Lastly, it is anticipated that the georeferenced 3D objects, as well as this 3D fossil visualization, may become part of an interactive museum exhibit in the future.

Terrorism continues to be one the most significant security threats of our time. Recent terrorism events include mass shootings and bombings in the U.S. and worldwide. First responders?law enforcement, emergency medical services, and fire services?are responsible for managing the chaos in the immediate aftermath of a terrorism event. Providing first responders with high quality, detailed information as quickly as possible could greatly enhance their ability to respond effectively. Recently, crowdsourced data available through platforms such as Twitter, Facebook, and other social media outlets, have emerged as a potential source to aid first responders following a terrorism event. The focus of this thesis is to determine if Twitter posts are a useful source of intelligence for first responders. Mining this readily available data could also be useful following a natural disaster. The utility of twitter data for first responders was explored using a case study of the events following the Boston Marathon bombing in 2013. Twitter data was collected via GNIP, a social media API aggregation company. Through text analysis and interviews with first responders, a list of relevant keywords was developed. Kernel density was used to determine density of tweets in relation to events that took place from April 15th through April 19th, 2013. Spatio-temporal analysis was conducted to show when and from where tweets were being sent on April 15th, 2013. Results show that on Monday through Thursday the greatest density of tweets was surrounding the bombsites; when events related to the suspects occurred on Thursday and Friday, the density of tweets around those events increased. The spatio-temporal results show that as the day progressed, the majority of tweets spread throughout the Boston Metropolitan area. The overall finding of this thesis is that crowdsourced data, such as Twitter, can provide potentially useful information to aid first responders following a terrorism event.

This study detailed the data collection, processing, and source comparison of DJI Unmanned Aerial System (UAS) drone data from different examples of topographical datasets for accuracy testing. Three datasets were chosen as they were characteristically different, these terrains were those typically encountered while surveying in the energy industry and are representative of terrain types encountered in the south Ohio area. More broadly they are comparable with other terrain systems. The system used to collect the UAS data consisted of a DJI Phantom 4 unmanned aerial vehicle controlled by DJI Ground Station Pro on an iPad Pro that input and monitored flight parameters. The processing used various software applications. These included Pix4D, which was the photogrammetry software used to convert the data into georeferenced mosaics, models, and point clouds. Additionally, Esri's ArcGIS and Idrisi Terrset were also used in performing analysis. The data was then analyzed to find correlation to LiDAR and ground control to compare elevation similarities. For the purpose of this study ground control points and LiDAR are considered the trusted source of reference accuracy and precision. Accuracy was assessed against the control material by inversion methods, geometry, and visual assessments. The testing concluded cohesive data precision, accuracy, and detailed the process of creating remotelysensed materials and their conversion to geometrically accurate data.

Bureau of Land Management policy regarding wildfire events on public rangelands dictates that burned areas are closed to livestock grazing until the vegetation in the burned area has reestablished itself. Ranchers and their supporters contend that extended duration of such grazing closures increases the likelihood of subsequent fire events during the grazing rest period. The ranchers attribute this effect to an over-accumulation of vegetation during the grazing rest period. With the goal of testing the claim made by ranchers, this project utilized fire history records, grazing allotment data, and remote sensing vegetation indices to identify and analyze potential rest period fires between 2000 and 2016 in and around the Nevada counties of Humboldt and Elko. GIS proximity tools were used to identify initial and subsequent fires on BLM grazing allotments which met the spatial and temporal requirements of a rest period fire. The four most likely candidates for rest period fires were selected for further examination as case studies. Scaled NDVI was used as an estimator of vegetation cover and change between selected initial and subsequent fires. Precipitation and land cover data were incorporated to provide further context. Three of the four fire perimeters showed increased vegetation cover when compared to similar nearby unburned sites during the second spring after the initial fires. This pattern suggests that increased fuel loads before the secondary fire may have been present. Evidence of cheatgrass and anthropogenic fire activity in the case study area suggest more complex explanations. Ways to improve monitoring and post-fire recovery through better record keeping, more complex sensors for satellite imagery, and targeted grazing research are discussed.

Caves historically have been one of the most difficult types of terrain for mapping and data acquisition due to the inability to use satellites, aerial imagery, or even accurate GPS receivers. Karst science typically relies on outdated survey methods, but advances in technology which allow for 3D models of terrestrial objects and terrain, are slowly making their way into Karst science. The most accurate method of remotely scanning areas and collecting accurate data, light detection and ranging (LiDAR) produces impressive and accurate 3D models and even detects sub-canopy elevation changes. However, its prohibitive costs and processing requirements make it unavailable to many. Close-range photogrammetry (CRP) is an affordable alternative given cost, but at the loss of accuracy in the 3D model produced.             While CRP with geo-referenced imagery can be used to produce 3D models of terrestrial landscapes and objects or “floating” subterranean objects, there are few studies that have utilized CRP in the entirety of an enclosed cave environment. This study examines a previous methodology used to create 3D models of terrestrial caves as a way to model underwater cave systems as well as terrestrial systems. The aim is to validate this methodology and apply it to different systems, with minor necessary adjustments. The photogrammetry data collection process utilized a GoPro Hero 5 camera and floodlights to collect imagery, which were processed using Agisoft PhotoScan Professional. High accuracy GPS receivers were used to collect cave entry coordinates to produce georeferenced models that were imported into ArcGIS. Traditional surveys were conducted to compare models. The methodology requires further modification and technical diver training to produce 3D models successfully of underwater cave systems via photogrammetry. Using a modified version of Jordan’s (2017) methodology produced promising results and 3D models of the terrestrial caves.

The People's Republic of China's (PRC) militarization of artificial islands in the South China Sea (SCS) represents a challenge to security of, and freedom of navigation in, international waters. Static defenses on these islands enhance Anti-Access and Area Denial (A2AD) efforts, allowing de facto sovereignty in the area sustained by successful radar coverage. While many A2AD tools may not be measured without direct access to the product, conventional radar structure heights may be measured remotely, allowing for indirect measurement of an adversary's radar range. Though estimates for these ranges have been published by various defense thinktanks, this study builds on shadow analysis literature to perform more accurate measurement and projection of radar ranges through use of remote sensing and trigonometry applied to imagery of SCS radar construction in late 2017. This study uses shadow analysis to measure radar tower heights combined with radio wave propagation equations to provide a viable alternative to rule-of-thumb estimation. This novel methodology is tested on radar arrays identified by the Center for Strategic and International Studies (CSIS) on three key islands in the SCS's Spratly Islands. Radar horizon range measurements provide a detailed analysis of radar coverage at various altitudes, showing that previously published estimates can differ from bespoke analysis by more than double. The study quantifies average range of radar arrays on artificial islands created by the PRC, finding the average radar to reach radar horizon in 23.82 km distance at 0 m altitude; equal to 249.47 km at 3,000 m, or 435.81 km at 10,000 m, respectively

Runners who strive to complete a marathon will need to make many sacrifices in their daily lives and train for months in order to accomplish such a physical and mental endeavor. Given this, having a successful race experience is pivotal. All the hard work could be compromised without the proper tools to help select and plan for the race itself. There are numerous resources that provide guidance for runners focusing on how to prepare physically for the distance, minimize injury and maximize performance. What is currently lacking are resources focusing on the individual needs of the runner and the logistical process of selecting and preparing for a race. The development of this Web GIS application used a geodatabase and Web GIS technology that allows a runner to personally select criteria to find a race that meets their needs, view races and elevation profiles on a map, select a 3D interactive view of the race courses to study the terrain, and view nearby lodging and dining options. Geospatial technology gives a runner a better understanding of the course and streamlines the travel process, reducing stress and increasing the likelihood of a successful and enjoyable race experience. After careful analysis of a runner's needs and the marathon selection process, and evaluating what techniques and methods should be used, a Web GIS application was developed to help facilitate the process for an enhanced race experience. In addition to providing a valuable tool for runners, this application provides a template for developers constructing a Web GIS application for any athletic or travel based event. Emerging technology will transform the Web GIS application into an even more powerful tool. Utilizing predictive analytics which incorporates data, statistical algorithms and machine learning techniques, patterns from the race course can be modelled and compared with local terrain to create similar courses for training purposes. It's invigorating to think of what effect the amalgamation of GIS technology into the athletic world will have on an athletes' experience.

Lyme disease is the most common vector borne disease in the United States. The incidence rate of Lyme disease has been on the rise since it was defined in 1977. From 2000 to 2016, there were over 18,000 cases of Lyme disease diagnosed each year. Of all the confirmed cases of Lyme disease in the United States, 95% occur in the Northeastern and Midwestern states. Lyme disease is contracted by a bite from an infected tick, Ixodes scapularis. This research aimed to find the hot spots of Lyme disease and the environmental risk factors, determine the counties that are hot spots in the Lyme disease rate and climate variables maps, and to create a model to test the influence of the variables. Past studies of Lyme disease created risk maps that centered on regression analysis. This study goes a step further to include trend analysis of Lyme disease and the environmental factors while considering spatial and temporal factors. This study investigated the spatiotemporal trend of the Lyme disease spread rate and environmental factors using hot spot analyses and local Moran’s I. A space time cube of these factors was generated and emerging hotspots over 16 years of time period (2000 – 2015) were analyzed. The hot spots were used to identify the correlations of Lyme disease and climate factors. An ordinary least square regression was used to evaluate the relationships between Lyme disease and the environmental risk factors to create an inferential model of Lyme disease. Spatial and temporal environmental risk factors included were precipitation, minimum, mean, and maximum temperature, latitude, longitude, percent forest cover, and year. The variables found to be most significant were year, longitude, latitude, and mean temperature, and explained 14.4% variance of Lyme disease rate in the study area. The significant spatiotemporal environmental factors identified provide researchers and public health officials with updated key factors, and can be used to educate the general public on high-risk areas in the northeastern United States.

Many agencies today still use outdated paper maps and construction plans to manage their utility systems. Those using outdated information and methods can benefit from a digital upgrade. Geographic Information System (GIS) is becoming a common solution for asset management and utilities because of its ability to combine spatial and non-spatial information all in one place. This project implements a GIS for the landscape irrigation system on the Music Academy of the West campus in Montecito, California. Campus staff were using a series of disorganized and incomplete paper maps to manage their irrigation system. They also had no information about where the main line and lateral lines were, which proved to be problematic during construction projects and pipe leaks. Transitioning the paper maps to digital GIS format proved to be a good solution. Using GIS, spatial data collection methods, campus staff knowledge, and existing construction documents, new hardcopy maps and an online web map application for irrigation management were developed for campus staff. In the future, the new irrigation map data can provide efficient solutions for future campus facility projects such as water usage analysis and leak detection device placement. Creation of GIS data for irrigation and other utility systems will likely continue to be a solution for effective asset management in the future.

With continuous advances in science and technology, there is high potential for a variety of agents to be used in bioterrorist attacks, making it difficult to prevent and mitigate the effects. Geographic Information Science (GIS) is an important tool in contributing to the preparedness, response, and recovery from bioterrorist attacks. GIS is beneficial in processing a significant amount of data for a multifactorial analysis and generating visual representations that indicate risk levels of designated areas, dependent upon specific variables throughout the area. Authorities such as health and human service agencies, Center for Disease Control (CDC), and the Department of Homeland Security (DHS) could utilize this information to decrease the effect of the attack and increase mitigation efforts, leading to a quicker recovery. In this analysis, GIS is used to assess and clearly portray the high, moderate, and low-risk areas for bioterrorism within certain parameters throughout San Diego County. The contributions of the resulting information include monitoring and surveillance as well as emergency preparedness, planning, and response. The specific parameters consist of aerosol dispersal of the biological agents, or pathogens, anthrax and plague with dissemination methods via devices such as airplanes, or ground detonation. This assessment comprises of locations of military bases or operations, population density, wind patterns, previous attacks, government buildings, public transportation, areas containing high profile people or projects, and areas in which the topography might influence the spread of the biological agent.

Archaeology allows us to see our human past and who we are as a people. This is a global narrative that spans the entirety of human existence. Many archaeological sites are delicate and are often unknowingly destroyed by human development. Because of this, pristine and protected islands offer a complete wealth of archaeological information. Stewardship programs and regulations set in place for protecting these cultural resources have been set into place on federally owned lands. San Nicolas Island and San Clemente Island, two of the Channel Islands owned by the United States Navy, are among the most well-documented and protected locations for archaeological sites in the United States. However, many of these sites are currently at risk from inundation and erosion. Global sea level rise not only potentially inundate the coastal zones but also accelerate geological erosion processes. To help the U.S. Navy understand and protect against the threats from these natural processes, this study aims to identify the at-risk archaeological sites on San Clemente Island and San Nicolas Island. A spatial-explicit Coastal Vulnerability Index (CVI) was developed from the ranked vulnerability score of environmental variables, including slope, inundation, generalized rock type, and vegetation, using a Geographic Information Systems (GIS). Based on the CVI, a Cultural Resource Vulnerability Index (CRVI) was developed to rank the coastal vulnerability of the archaeological sites on the two islands The results of the CRVI showed that 3.6% of the archaeological sites on San Nicolas Island and 19.2% of the archaeological sites on San Clemente Island fall within the Highly Vulnerable to Very Highly Vulnerable categories. The CRVI informs the land managers in the U.S. Navy an earlier response time to save these at-risk sites that may be completely destroyed in the next 100 years. With the result from the CRVI, further actions can be taken to mitigate and/or

When project proponents wish to assess a development site for jurisdictional wetland impacts, they are traditionally left with two options: a wetland determination or a delineation. A wetland determination is customarily a desktop assessment of the site including, but not limited to, the following datasets: National Wetland Inventory (NWI), National Hydrography Dataset (NHD), National Resources Conservation Services Soil Survey (NRCS), topographic maps and satellite imagery. A wetland delineation assesses the presence of hydrophytic vegetation, hydric soils and hydrology during field evaluation. The NWI is typically used to determine where existing wetlands are in order to determine if they qualify as jurisdictional wetlands. This allows project proponents to either take the appropriate avoidance measures to reduce impacts to the wetland or determine if a full wetland delineation is required to apply for a Section 404 permit. In some cases, NWI maps have not been updated for up to 30 years, and these mapped wetlands are limited by conditions that were present at the time the aerial imagery was taken. This thesis shows that by incorporating unmanned aerial systems (UAS) into a wetlands determination, wetland specialists and project planners can capture current conditions of the development site (i.e. topography, disturbance, land cover, etc.) within efficient time frames and assess the potential extent of a wetland(s). This allows project proponents to avoid the cost and time restrictions that come from a full wetland delineation. The UAS imagery was compared to historically mapped wetlands still present; UAS improved placement of wetlands on the landscape and had on average a 76.5% overlap with delineated wetlands. Future research into buffer distances, topography, seasonality and thermal imagery could improve this overlap. With the aerial imagery from current conditions, wetland specialists can assess potential wetland extent, hydrology, highwater marks, and coarsely classify vegetative condition.

Over the years researchers have examined greenspace using definitions of varying breadth and various measures to capture that breadth. This study compared three such definitions and associated measures to assess their similarities and differences. It sought to spatially examine these measures and determine whether the results they produce are statistically interchangeable or different in various ecoregions and urbanization levels, as well as observe any notable nuances between types. For definitions and measures, the study used inventory (a polygon shapefile of parks), usage-based categorical (a raster data set of classified vegetated land covers), and biophysical (satellite imagery based Normalized Difference Vegetation Index (NDVI)) data. It tested these three types within neighborhoods in four different regions in the state of California. The regions chosen represented the north and south coast, inland desert, and central valley. Within each region, the data sets were tested in urban, suburban, and rural areas. The amount of greenspace represented by parks, vegetated land use classes, and spring Landsat 8 NDVI imagery within each tract was measured and the averages within each measurement type were compared to one another. It was expected that land use and NDVI data would show statistically greater amounts of greenspace cover in rural and suburban areas, but that parks data would show more in urban areas due to sensor resolution limitations. If true, this would suggest regional variation in measurement type comparability. It was also expected that additional type-specific strengths and weaknesses would emerge. This information will be useful in determining whether new combinations of greenspace measurements might prove fruitful. After analysis, the study found NDVI provided a statistically higher measure of greenspace overall, although there was some variation in the discrepancy between measures across area types.

Cities throughout the United States have adopted historical designations in order to protect the historic architectural resources and promote economic development of areas that carry a cultural significance to their communities. Detroit, a city in steep economic decline from 1970 until 2015, has also attempted to use historical preservation to promote economic development in particular neighborhoods. The role of historic preservation has rarely been considered in a city in steep, ongoing economic decline. The study presents spatial analysis techniques that can help determine the association, if any, of historical district designations with neighborhood rise or fall. By using various approaches to count structures and measure preserved space within census tracts, a difference-in-differences (DiD) analysis and an ordinary least squares regression model was developed to test the association of preservation and neighborhood status change from 1970 to 2015. The results indicate that census tracts with a historic designation showed less decline and quicker improvement in neighborhood status when compared to census tracts with no protections. To further corroborate DiD results, ordinary least squares analysis indicated statistically-significant relationships between the percentage of historical district coverage and historically protected building counts and changes in all but two indicator values, but these results should not be accepted as evidence of causality because there is significant spatial autocorrelation. Also, Detroit’s socio-economic conditions differ from other metropolitan statistical areas in the U.S. Further research is needed in other cities during periods of economic decline and with additional control variables.

Research has often examined geographical barriers to healthcare accessibility. These examinations, however, are usually focused on primary care and urgent or specialty care. This study focuses on access to vision care and services with the goal of bridging the gap in research for this category of healthcare. Spatial accessibility for Kaiser Permanente members was examined using the Enhanced 2 Step Floating Catchment Area (E2SFCA) method. This method has been used in previous studies to examine spatial accessibility of patients to healthcare services. It examines both supply (the amount of services or providers available to provide services) and demand (patients who may or have used such services). This study also examined the differences between using ZIP codes and Census tracts as the base geography and for understanding how this choice is likely to affect the performance of the E2SFCA method and the final outputs. The analysis showed that the southern region of the Riverside Medical Services Area (MSA) has a shortage of optical services and that members must travel longer distances for these services. Future research should further analyze the accessibility of the members living within the Riverside MSA to vision services offered by Vision Essentials of Kaiser Permanente.

This thesis aims to improve the quality of tools available to the Air National Guard, more specifically the RC-26 mission management system (MMS). The RC-26 is an unconventional aircraft doing an unconventional mission for the military, working directly with law enforcement agencies to provide an airborne video camera for counter-narcotics activities. This aircraft has been doing this mission since the late 1990's and has undergone many hardware and software upgrades since then. However, these upgrades commonly take many years to accomplish, resulting in operators using obsolete systems and outdated information stored on the aircraft's computer systems. In some cases, operators even lack the knowledge of how to utilize the systems or update them to better accomplish the mission. The purpose of this thesis is to help rectify those shortcomings and create a simple and repeatable process for maintaining and updating those systems with the latest, most up-to-date street maps and aerial imagery from all sources available on the internet and thus improving the aircrew's effectiveness. It does so by sourcing compatible street data and imagery from government sources that is compatible with a number of government systems. Additionally, since the fleet of aircraft are spread throughout the country, this work creates an instructional aid so that the process can be fully understood and replicated. Using the sources and procedures developed in this thesis have been applied to real world law enforcement support as well as humanitarian mission in support of Hurricane Relief in Puerto Rico.

With the recent abundance and democratization of high-quality, low-cost satellite imagery comes the distinct need for a way to analyze and derive insight from this ever-growing torrent of data. Machine learning technologies and methods are now frequently applied to large datasets to accomplish such varied tasks as language translation, fraud detection, disease diagnosis, and automated driving. This project proposes a means to apply these same technologies to automatically detect and digitize features within satellite imagery. An end-to-end machine learning and web application framework was developed to detect, extract, and digitize arbitrary classes of geospatial features. This system is composed of a web user interface which allows users to source true-color satellite imagery and existent digitized feature data and subsequently use these data to train a machine learning model that will "learn" to automatically identify features within new imagery. This involved the development of both a web application user interface and a specific type of machine learning algorithm termed a neural network that has been shown to excel in image recognition tasks. Following the identification of these features from satellite imagery, features may be exported to a geospatial database for storage and further analysis. This system and provides the foundation for a significant retooling and augmentation of manual geospatial feature digitization workflows and creates new opportunities for geospatial analysis by deriving features from aerial images rapidly en masse.

Understanding how human needs and innovations affect the environment plays a fundamental role in ensuring the longevity of our earth's natural features. While expansion and urbanization help to enlarge human influence, and allow for the growing human population, special care must be taken to maintain a mutually beneficial relationship with earth systems and physical phenomena. This relationship between growing population and environment can be seen as more and more people make their way to the coastlines of the world to support livelihood as well as recreation. However, living along the coast sometimes requires modifications to ensure the protection of those deciding to live there. This study examines the management of one such relationship between the everchanging coastline of Galveston Island and the Galveston Seawall set as the first line of defense against storm surges and rising tides. While seawalls are meant to protect coastal populations form extreme flooding events and hurricanes, the long-term result of a seawall is often the erosion of the natural beaches which those living along the coast have come to enjoy. Galveston Island represents a city whose seawall has stood for over a hundred years, built just after the Great Storm of 1901. This spatial study analyzes the coastal conservation and protection provided by the Galveston Seawall and Groins. Using sediment supply, beach area, landuse and coastal velocities, a site suitability analysis was generated showing locations prone to sediment deposition and creating a model for how these features interact with the gulf shoreline. From the results, it is recommended that local geography and earth systems be analyzed prior to the construction of coastal structures to avoid costly unforeseen coastal changes in the future.

Federal, state and local officials are planning for a (M) 7.8 San Andreas Earthquake Scenario in the Southern California Catastrophic Earthquake Response Plan that would require initial emergency food and water resources to support from 2.5 million to 3.5 million people over an eight-county region in Southern California. However, a model that identifies locations of affected populations—with consideration for social vulnerability, estimates of their emergency logistical resource requirements, and their resource requirements over time—has yet to be developed for the emergency response plan. The aim of this study was to develop a modeling methodology for emergency logistical resource requirements of affected populations in the (M) 7.8 San Andreas Earthquake Scenario in Southern California. These initial resource requirements, defined at three-days post-event and predicted through a probabilistic risk model, were then used to develop a relative risk ratio and to estimate resources requirements over time. The model results predict an “at-risk” population of 3,352,995 in the eight-county study region. In Los Angeles County, the model predicts an “atrisk” population of 1,421,415 with initial requirements for 2,842,830 meals and 4,264,245 liters of water. The model also indicates that communities such as Baldwin Park, Lancaster-Palmdale and South Los Angeles will have long-term resource requirements. Through the development of this modeling methodology and its applications, the planning capability of the Southern California Catastrophic Earthquake Response Plan is enhanced and provides a more effective baseline for emergency managers to target emergency logistical resources to communities with the greatest need. The model can be calibrated, validated, generalized, and applied in other earthquake or multi-hazard scenarios through subsequent research.

The creation of Hot Streets can positively influence the crime reduction efforts by law enforcement agencies (LEAs) by decreasing patrolled Hot Spot areas and more directly focusing efforts at the street level. As there has been no easy way of determining Hot Streets, police officers patrol general areas that vary in size and difficulty of patrol. The purpose of this study is to create a model within a GIS, particularly ArcGIS Pro, for all users who wish to accurately and efficiently analyze crime patterns on a street level. The model shows all users, especially the LEA tactical analysis department, a simple but effective means of using a GIS to improve current spatial crime analysis methods by the addition of Hot Streets. This study demonstrates how to analyze and automate the creation of Hot Streets within the ModelBuilder pane for the city of Atlanta, Georgia. The research provides users with places for the acquisition of GIS data, methods and input parameters required for processing data prior to incorporation in the model as well as within the model, and the proper sequence of tool utilization for analysis within the model. This process resulted in Hot Street maps with several streets classified based on the crime cluster confidence levels of 90% and above for the city of Atlanta. The Hot Street provides results for seven confidence levels; which include high and low value crime clusters at 90%, 95%, and 99% respectively, and a final group of streets without a significant cluster. The developed model was found to be an excellent tool in analyzing crime patterns on a street level and creating the Hot Street maps at different scales. Both LEAs and civilians can utilize the developed Hot Street implementation, as it provides a way to reduce crimes through hot street policing and crime prevention through environmental design.

Paper birch (Betula papyrifera) is a dominant specie within Northern Minnesota's Laurentian mixed forest. Though these trees are common place, paper birch populations have been in decline for the past couple decades along Lake Superior. Due to the reduced replacement rate of this specie, organizations are implementing management strategies to promote healthy forests. This thesis investigates remote sensing techniques to predict paper birch locations remotely. The thesis tests individual specie level spectral signature effectiveness to classify community level data of the same informational group. The project uses hyperspectral Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) flights data for its imaging platform. Two spatial resolution scenes were classified for this project. A 4.3 m pixel resolution image was used for defining spectral signatures based on ground truth data. Then a lower resolution 16.5 m image was used to apply the produced paper birch signature as a classifier to test functionality of these methods on known paper birch communities. Pixels were used as the final classification unit. A linear unmixing soft classification was utilized to produce percent signature contained within pixels. The classification resulted in ~93% of forest plots containing some pixels with 95% similar spectral signatures to paper birch. Total classified coverage of validation forest plots was low with only ~30% covered with 75% similar signature or greater. The long-term objective for this project is to automate specie identification to monitor trees. Further research is needed to streamline classification and refine procedures, yet current findings can help forest managers and conservationists identify priority sites to both map current specie distribution and implement restoration activities.

Renewable energy is becoming increasingly important as energy prices and air pollution increase globally. Wind and solar power have become more affordable and efficient. However, current renewable energy production cannot bear the weight of the world's growing need for energy unless we can effectively tap the world's greatest source of energy: the ocean. Wave energy converters are technologies designed to harness the energy from the ocean waves. This study aims to help energy resource planners identify the most efficient locations for wave farms near the coast of Southern California. Current studies with the similar goals either only used wave data as the variables during the decision making process or considered other variables but omitted the wave data. Few were found to include both, yet those too are lacking in the full scope. In this study, wave power data as well as environmental and legal limiting factors were included in wave farm site selection. These limiting factors, along with the wave data, consisted of seven individual layers that were each given weights according to their importance in regards to a PowerBuoy? wave farm and then combined together using a weighted overlay. The results of this overlay were used to select five areas with the most potential as a suitable location for a wave farm. A simple cost comparison was then conducted to determine which site was the most suitable. It was determined that a site roughly 25 kilometers due south from Point Conception was the best candidate. However, the conditions in the sea off the coast of Southern California are less than ideal for wave farms with the current state of wave energy conversion technology due to a relatively low level of wave power caused by the complex geography of the region.

Renewable energy is becoming increasingly important as energy prices and air pollution increase globally. Wind and solar power have become more affordable and efficient. However, current renewable energy production cannot bear the weight of the world’s growing need for energy unless we can effectively tap the world’s greatest source of energy: the ocean. Wave energy converters are technologies designed to harness the energy from the ocean waves. This study aims to help energy resource planners identify the most efficient locations for wave farms near the coast of Southern California. Current studies with the similar goals either only used wave data as the variables during the decision making process or considered other variables but omitted the wave data. Few were found to include both, yet those too are lacking in the full scope. In this study, wave power data as well as environmental and legal limiting factors were included in wave farm site selection. These limiting factors, along with the wave data, consisted of seven individual layers that were each given weights according to their importance in regards to a PowerBuoy™ wave farm and then combined together using a weighted overlay. The results of this overlay were used to select five areas with the most potential as a suitable location for a wave farm. A simple cost comparison was then conducted to determine which site was the most suitable. It was determined that a site roughly 25 kilometers due south from Point Conception was the best candidate. However, the conditions in the sea off the coast of Southern California are less than ideal for wave farms with the current state of wave energy conversion technology due to a relatively low level of wave power caused by the complex geography of the region.

Looting of archaeological sites is a global problem. To quantify looting on a nationwide scale and to assess the validity and scope of the looting reports and modern encroachment, satellite archaeologist have turned to mapping looting from space. High-resolution satellite imagery has become a powerful tool and resource for monitoring looting and site destruction remotely and proves to be an independent way to cross check and analyze against varied and unreliable reports from media and government agencies. It is estimated that over a quarter of Egypt's 1100 known archaeological areas have sustained major damage and site destruction directly linked to looting. The organized looting and illicit trafficking of art and antiques, known as cultural racketeering, is a multi-billion dollar worldwide criminal industry that thrives in Egypt during times of political and economic turmoil and potentially funds drug cartels, armed insurgents, and even terrorist networks. This study analyzes methods used to monitor site looting at the archaeological site of al-Lisht which is located in the Egyptian governorate of Giza south of Cairo. Monitoring damage and looting over time has been largely dependent upon direct human interpretation of images. The manual image comparison method is laborious, time consuming, and prone to humaninduced error. Recently, partially-supervised methods using deep convolutional neural networks (CNNs) have shown astounding performance in object recognition and detection. This study seeks to demonstrate the viability of using deep convolutional neural networks (CNNs) within the field of archaeology and cultural heritage preservation for the purpose of augmenting or replacing the manual detection of looting. It brings recent advancements from the field of Artificial Intelligence to an applied GIS challenge at the intersection of remote sensing and archaeology. The objective is to show that CNNs are a more accurate and expedient method for the detecting of looting with wide-ranging application beyond this specific research.

Geographic Information Systems (GIS) can greatly strengthen Safe Routes to School (SRTS) programs by helping stakeholders to visualize service areas and study the process of walking to school. Information from a GIS can help drive policies and change processes at a district level. The purpose of this study is to provide a framework for how GIS, particularly Esri's Network Analyst, may be used by a school district in SRTS programs. This study demonstrates how to analyze and visualize the process of students safely walking to school for two elementary schools in Chula Vista, California. This framework provides districts with procedures on how to acquire GIS data, preprocess data for Network Analyst, and analyze data by setting up a network with appropriate barriers and impedances. It shows district administrators a simple yet effective means of using a GIS to strengthen SRTS programs. This study resulted in maps of several routes within the Harborside Elementary and Wolf Canyon Elementary school zones. The maps and outputs helped to determine where the model worked well and where there were areas for improvement. Overall, Esri's Network Analyst extension was found to be an effective tool in modeling the safest routes to school; however, each school zone needed model customization. This research also emphasizes factors that school districts would need to consider in the use of GIS for SRTS programs. Such implementations of GIS may help school districts better understand the process of students walking to school and help district administrators to make better-informed decisions regarding SRTS programs.

Yak are a high yielding but underutilized commodity in American agriculture; a sector that could benefit both economically and ecologically from diversification. Diversification in agriculture is important to help alleviate stress on the environment and provide economic security. This analysis used fuzzy overlay to conduct a statewide site suitability analysis in Illinois to locate the most favorable counties and subcounty divisions to begin yak-based agriculture. Yak-based agriculture refers to a farming or ranching operation where yak are raised as a commodity. Based on a review of literature regarding the conditions for successful yak-based agriculture, the fuzzy overlay analysis undertaken here incorporated both continuous data forms, particularly the climate criteria of temperature, precipitation, and vapor pressure deficit, and the categorical data of cropland use and soil associations. While initially considered to be key criteria for successful yak-based agriculture, the factors of slope and market proximity were removed from this analysis. Slope was not included because nowhere in the study area was the slope a limiting factor. Market proximity was not included due to the dense road network and easy road accessibility throughout the state. However, it is noted that these factors should be incorporated in any future studies that replicate this approach. In the final results, Will, Kankakee, and Iroquois counties were found to be suitable locations for potential yak-based agriculture but not highly suitable as Illinois' climate is not similar to the yak's native range of Tibet. Conclusions from this analysis and similar ones undertaken in the future have potential to assist county farm bureaus in better understanding how to diversify farming to protect the farmer from potential economic disasters and the soil from the harmful effects of monocropping.

This research hypothesizes that a data-rich, geographically explicit agent-based model can provide context for archaeological finds when the archaeological record itself is too incomplete or damaged to do so. It specifically seeks to address the problem posed by disparate but mounting evidence of earlier than expected sea crossings in the Mediterranean. Hundred-thousand-year-old lithic evidence of human presence on islands encourages the revisionist view that the Pleistocene Mediterranean was less of a barrier and more of a facilitator for travel than previously thought. Nevertheless, it fails to answer Mediterranean archaeologists' questions about how and why. This research shows how an agent-based model can be designed to allow archaeologists to formulate and test theories about the ways the environment could have created opportunities for early sea crossings. It demonstrates the process of designing and building this model in R and NetLogo. Preliminary results show that this model can be used to help archaeologists better understand the revisionist conceptual model of sea crossings in the Pleistocene Mediterranean.

Stone tools and their waste products, due to their durability and their importance to everyday prehistoric life, are key elements found in archeological sites. By knowing the locations of the stone outcrops and the distribution of the stones deposited in archaeological sites, researchers will attain a clearer understanding of prehistoric people's daily lives. In this study four stone materials, Burlington chert, Mill Creek chert, Cobden/Dongola chert, and Kaolin chert, are tracked from their outcrop location in southern Illinois to the archeological sites where prehistoric peoples deposited them. The raw material taken from these outcrop areas has been found as much as 100 miles away even when other sources of chert are closer. This is evidence of the choices made by prehistoric peoples for one chert type over another. This research was conducted in order to understand the stone material selection process, the distance prehistoric people will go to obtain a specific chert type, and the temporal affiliation of these choices. Included in this study is an endeavor to find the most probable outcrop areas for each chert type. The outcrop prediction model broke down the landscape characteristics including slope, waterways, and geology and identified the areas of highest probability of finding these cherts. The research also sought to identify the distance chert was transported from its outcrop location. By using archaeological site chert data, the distance that the outcrop material was transported in the study area was identified. Additionally, a distribution pattern of the material across the landscape shows areas where each chert type was more heavily concentrated. Finally, by researching the distances and distribution of chert during specific cultural components, inferences made by archeologists concerning the distribution of these specific cherts are proven.

This analysis of the local food environment in Lane County, OR aimed to investigate inequalities associated with access to healthy food. The findings suggest that the problem is complex and is not simply a lack of healthy food stores. Retail food environments evolve quickly and research approaches to evaluate the phenomena are nimble with advanced technology and high quality data. Spatial access to healthy food is a key factor for dietary decisions. Previous research linked diet related diseases in disadvantaged communities to disparities in food access. Disadvantaged residents were associated with low access to healthy food outlets and high access to unhealthy food stores. Neighborhood food access was tracked through statistical analysis of economic and demographic characteristics that were collected in the federal census. This analysis quantified the food environment in Lane County, Oregon. The primary assessment measured residential proximity to five different food store types over the road network in Esri's Network Analyst. The distances were aggregated into Census Block Groups to determine whether access to healthy food decreased in disadvantaged neighborhoods. This research aimed to fill the gap in the literature for distance-based food access analyses using residential address points at a local scale. This work employed systematic methods that addressed food retail dispersion across heterogeneous space to determine food outlet presences and absences at various distance bands across the study area. This research contributes to methodological developments that would eliminate the standard practice of compartmentalizing urban and rural food environment research into silos that are evaluated separately. The primary finding of the study was that neighborhoods in Lane County characterized as high deprivation with higher minority compositions had better access to healthy food store types. Future research should consider the affordability of healthy foods and include farmers' markets, roadside stands, and community supported agriculture.

This study compares the use of cadastral land use data with remotely sensed land cover data for urban sprawl studies using the Shannon's Entropy spatial metric. Many rapidly urbanizing countries lack the technological or economic infrastructure necessary to establish and maintain digital cadastral systems, so remotely sensed land cover data may be a viable option for performing urban growth and urban sprawl studies due to its accessibility, cost, and thematic consistency. Shannon's Entropy is a commonly used metric for measuring sprawl in regions outside of the United States, where cadastral data is not available. Few studies have compared land cover and cadastral land use data using Shannon's Entropy as the main comparison metric. The study uses Model Builder in ArcGIS to perform Shannon's Entropy calculations on the metropolitan areas of Minneapolis and Chicago during the period from 2000 to 2011. The calculation uses the proportion and dispersion of low-density land development within the study area to quantify sprawl. The study cities are divided up into buffer zones, and the proportion of low density land development is measured for each zone. This study found that there was no significant difference between the Shannon's entropy results between land use and land cover. The results suggest that land cover data may be suitable for urban footprint studies in regions where cadastral data is not readily available or otherwise unavailable. This study also found that both metropolitan areas had high entropy values, but entropy did not significantly increase over the study period. These results help inform the broader literature on usable data types for urban footprint studies, as well as the use of Shannon's entropy for such studies.

Smartphones have revolutionized the way users interact with the world and have helped pave the way for hundreds of new and exciting mobile applications. A complex array of sensors exists within smartphones, including GPS, barometer, accelerometer and other positional sensors that are leveraged by these mobile applications. These sensors are capable of providing location, speed, gradient, altitude, and acceleration data that are foundational for providing a new generation of mobile fitness applications. One such example is the development of cycling power meter applications within the sport of road biking that provides new insights into the real time power expenditure and overall cycling efficiency. This research focuses on the potential of using a smartphone and opposing force power meter (OFPM) as a replacement for traditional and expensive direct force power meters (DFPM) that have been the "de facto" standard over the last ten years. Field collected power meter cycling data, combined with spatial analysis, is used to compare various dimensions of power meter accuracy, GPS road network accuracy, elevation agreement, and cost. The overall results of this field study showed that using a smartphone power meter application performed within +/- 10% on average when compared to a traditional DFPM meter, but only when the application had access to high quality location and speed data from the smartphone's GPS sensors. The results also showed that on average, the OFPM system performed within +/- 2% on average when compared to the DFPM reference power meter but was challenged with data latency on quick changing terrain and accelerations. Concluding the research, a summary analysis is provided as a way for cyclist to quickly understand how well each power meter performed and to determine if a specific power meter system is better suited for a rider's individual needs.

Humanitarian Assistance and Disaster Response (HA/DR) Operations executed by the military forces of developed nations have proved to be powerful instruments of foreign policy. Militaries bring a significant vertical-lift logistical capability to HA/DR, in the form of traditional helicopters and modern tiltrotor aircraft. Military planners of HA/DR employ advanced geographic information systems (GIS) when planning Helicopter Landing Zones (HLZs). GIS continue to improve; however real-time ground-truth HLZ surveys would add a level of detail that may prove crucial to helicopter crews. Crowdsourcing efforts such as the Humanitarian OpenStreetMap (OSM) Team (HOT) have emerged as a highly effective means of gathering geospatial information about impacted regions in the immediate aftermath of a disaster. This research endeavors to develop, test, and validate a series of straightforward, easily understood procedures for conducting a HLZ survey, which then can be made available to HOT volunteers. Military planners employ GIS and remote-sensing imagery to select potential HLZ sites. These sites are imported to OSM, where volunteers can obtain data as to their location. Volunteers can utilize the techniques developed in this research to conduct a ground-truth survey of the HLZ and provide the results back to the HOT. The HOT can verify these inputs and link them to the HLZ in OSM. This thesis describes the creation and validation of these processes in a study area focused on Hawaii County, Hawaii.

The Majestic Yosemite Hotel, formerly known as the Ahwahnee, is a National Historic Landmark located in Yosemite National Park, California, USA. Built in 1927, the hotel attracted rich and wealthy individuals to help gain financial support for the National Park Service idea of protecting wild spaces for future generations. To this day, the hotel stands as one of the National Park Service's most historic lodging units, providing luxury accommodations and services to park visitors. In November of 2016 Yosemite Hospitality, Yosemite National Park's Concessionaire requested a mobile application to educate visitors on the cultural and historical significance of the hotel to support the goals of the Long Range Interpretive Plan. Yosemite Hospitality was the client for this project, and the application was developed in direct consultation with Yosemite Hospitality's Interpretive Services Department from November 2016 until August 2017. Several indoor positioning technologies and Augmented Reality services were tested to deliver educational content based on user mobile device locations and camera orientations. The processes tested the Anyplace indoor positioning service, IndoorAtlas indoor positioning service, BlueCats beacon services, Vuforia Augmented Reality services, and the gaming engine Unity. Testing and development occurred on both Android and iOS devices with development in Javascript, C#, Swift, and Objective C. As part of this thesis work, a historical model with digital furniture scans was also completed to preserve the current conditions of the hotel's original furniture. These scans were based on the Structure Sensor manufactured by Occipital. This thesis documents the development and testing of the Majestic Virtual Tour Application and the historic furnishings model built for the Majestic Yosemite Hotel in fulfillment of the Yosemite Hospitality project.

Recovery from wildfires is related to a series of interacting factors. This study was conducted to reproduce and attempt to improve upon the work of Casady et al. (2010) by building a regression decision tree model for predicting post-fire recovery based on interacting environmental factors using two spatial resolutions. Mimicking the efforts of Casady et al. in evaluating post-fire vegetation regeneration rate, their term has been renamed throughout this study as ReGreen Rate, since this is a more accurate representation of how the imagery can be interpreted. This present study used a combination of ArcGIS and R to prepare data from 30 m and 240 m spatial resolutions and analyze model attributes' impact on recovery rates. This study answers two questions. First, does the use of higher spatial resolution data create a more accurate regression tree model predicting the post-fire ReGreen Rate? Second, do different indices of fire severity show a different result in model accuracy? The resulting models all demonstrated a strong correlation between fire severity and rate of vegetation recovery, where greater fire severity lead to faster recovery. As for the first question, 30 m spatial resolution data did provide a marginally more accurate predictive model. However, the model built from the 240 m spatial resolution data was nearly as accurate as the model developed from the 30 m spatial resolution data when applied to the 30 m data. Second, different indices of fire severity did not provide statistically different accuracy in the resulting model. Further research into modeling various forest recovery rates could be useful in constructing generalizable models based on 240 m data to produce a good prediction of recovery for application in forest management, enabling targeted areas for post-fire replanting and optimizing resources allocation.

The City of Irvine is one of the largest and earliest planned communities in the United States. It began in the 1960s after the University of California agreed to put their newest campus on land in the Irvine Ranch. The Irvine Company developed a General Plan for a small city of 10,000 people around the university but eventually expanded that plan to include the entire 100,000-acre Irvine Ranch. Many New Towns movement principles were followed. When the city incorporated, the new city council did not start from scratch but built upon the Irvine Company's master plan. This area was unique for a planned community in that it was huge, mostly undeveloped, and mostly under one landowner. GIS is used in this study to digitize and compare the 1973 General Plan of the Irvine Company with the 2017 land use database to determine if and where land use changes have taken place. Current parcel data was compared with the 1973 Irvine Company General Plan map to enable the tracking of changes for each parcel, if any. Extract Values to Points was used to pull values from the historical map into the current land use database. A pivot table was used to build a matrix of land use change pairs. The first research objective was to compare these two maps and locate changes. The second research objective is to see whether there are any trends in land use changes. This study found an increase in the amount of land dedicated to Open Space and a surprising decrease in residential density in a few parts of Irvine. Also found was a new trend in land use where residential units are being built inside the Irvine Business Complex. The resulting database could be used for future studies concerning one of the biggest and oldest planned communities in the United States.

One of the most important resources at a commercial service airport is the airport pavement. An airport has the responsibility of ensuring that aircraft are able to safely land, taxi, and takeoff by ensuring that the airport pavement is in a safe and serviceable condition and that the life of the pavement is maximized. With the continuous need to efficiently manage pavement at commercial service airports to extend the service life of pavement while reducing rehabilitation costs, more airports are turning to technology to assist with these tasks. However, the cost to implement an asset management system or a pavement management system can be extremely expensive and they often do not include GIS functionality as a standard feature. Though, with the recent growth of Web GIS technologies, commercial service airports can now implement a costeffective solution to managing their airport pavement. This research demonstrates how to develop a Web GIS application for airport pavement management for use at a commercial service airport using a low-cost, Software as a Service (SaaS) based Web GIS platform. The development includes the design of an airport pavement GIS data model. Esri's ArcGIS Online SaaS-based platform was chosen for the development of the Web GIS application that is userfriendly and easily accessible via any web browser, tablet, or mobile device. With the Airport Pavement Management Web GIS application, airport staff have the ability to visualize existing pavement conditions that enable them to make more informed pavement maintenance and rehabilitation decisions, visually compare previous inspections and detect trends, and allow them to direct the timely repair of deteriorating pavement and extend the life of the pavement. The development of this application also provides the foundation for uses in other domains such as disaster response or emergency management operations, in addition to supporting future GIS integrations with Smart Infrastructure technologies.

Light Detection and Ranging (LiDAR) technology combined with high-resolution differential Global Positioning Systems (dGPS) provide the ability to measure coastal elevation with high precision. This study investigates the use of LiDAR data and GIS to conduct time-series analyses of coastal sediment volume shifts during the 2006-2007 El Niño winter, Summer of 2007 and following 2007-2008 La Niña winter in the Oceanside Littoral Cell (OLC). The OLC, located in Southern California, spans from Dana Point to La Jolla and includes over 84 km of coastline. The ability to quantify sediment volume changes contributes to the scientific understanding of the role El Niño storms play in the OLC sand budget. This study provides a method to analyze LiDAR data to evaluate coastal geomorphologic changes over time. Additionally, identifying specific areas of coastal beach erosion associated with historical El Niño events can aid beach managers, planners, and scientists in protecting the valuable coastline. LiDAR datasets were prepared and formatted which included ground classifying millions of elevation points. Formatted datasets were inputted into an Empirical Bayesian Kriging (EBK) model, creating high-resolution, 1-meter grid cell, Digital Elevation Models (DEMs). The EBK model also incorporated uncertainty into the workflow by producing prediction error surfaces. LiDAR- derived DEMs were used to calculate sediment volume changes through a technique called DEM differencing. Results were visualized through a series of maps and tables. Overall results show that there was a higher rate of beach sediment erosion during the 2006-2007 El Niño winter than the 2007-2008 La Niña winter. Sediment accretion was evident during the intermediary Summer of 2007. Future applications of this study include incorporating bathymetric datasets to understand near-shore sediment transport, evaluating sediment contribution through cliff erosion, and conducting decadal scale studies to evaluate long-term trends with sea level rise scenarios.

This thesis performs a comparative analysis of traditional models of food access and a proposed model of food access that uses volunteered geographic information (VGI). Moreover, food businesses are often manually classified, which limits the number of businesses used for a given study. This thesis explores VGI as a potential improvement in the classification of food businesses. Field research was conducted in a subset of the selected facilities in order to determine the actual quality of the data retrieved from the experimental sources. The goal is to create a more nuanced and accurate representation of food access for a given person in a given place. Finally, data is compared for areas with different socio-economic conditions. Median income, car access, and percent minority from the 2010-2014 American Community Survey (ACS) 5-year estimates were used to define contrasting study areas. Two census tracts in Los Angeles were selected for the study area using these criteria: (1) an affluent area near La Cañada; and (2) a less affluent area in South Los Angeles. This thesis explores the quality and completeness of three data sets for census tracts with contrasting socio-economic conditions in order to identify whether or not problems exist with traditional methods and data. Furthermore, this thesis compares the data from census tracts with contrasting socio-economic conditions in order to determine whether or not the data varies based on the community served. The results of this thesis indicate that VGI does not represent a significant addition to commercial data because so few of the businesses are represented in the VGI data set. Moreover, the use of North American industry classification standard (NAICS) codes to classify businesses proved to be problematic. Specifically, numerous businesses that were classified as super markets or grocery stores were in fact smaller than convenience stores and sold fewer items. Finally, sentiment analysis of reviews will require a larger data set and specifically trained models in order to be evaluated further.

How can cities improve neighborhood quality after years of decline? One prominent attempt is the Minneapolis Neighborhood Revitalization Program (NRP) established in 1991 that earmarked $400 million over 20 years for neighborhoods to engage residents and create plans to improve the community. Previous studies evaluated the NRP program, but were completed too soon for the program to have a noticeable impact. Additionally, reviews of the first decade of implementation completed by 35 of the 67 neighborhoods assessed the success of the program, but these documents mainly served marketing and accountability purposes. This study adds to the critical appraisal of the NRP program by using census data and indicators for neighborhood income, home value, rent, and vacancy rate to examine whether or not the City of Minneapolis increased neighborhood quality. Propensity score matching paired Minneapolis study site neighborhoods with similar neighborhoods in St. Paul and difference-in-differences and hot spot analysis determined any significant changes in Minneapolis and its neighborhoods from 1990- 2014. Regression models explored the relationship between each indicator and variables for NRP participation, amount of NRP funding, number of days participated in the NRP, and neighbor funding levels, and spatial analysis explained why some neighborhoods were more successful than others. Results show that Minneapolis performed better than St. Paul during the study period, and that some neighborhoods in the city experienced statistically significantly greater improvements, most notably the neighborhoods in downtown. Based on this analysis, the study recommends solutions to improve future iterations of this program in other locales.

As anthropogenic climate change continues to accelerate, long-term and large scale monitoring of glaciers is crucial. The Taku Glacier offers a unique opportunity to apply remote sensing methods to a glacier with a long history of advance. The behavior of the Taku Glacier has been historically out of phase with regional climate and other proximate glaciers, which have undergone recent, and in some cases dramatic, retreat. Although remote sensing has been established as an effective tool for glacier monitoring, few studies have applied these methods to such a large glacier with so many diverse facies. This study establishes the effectiveness of using remote sensing to quantify long-term changes in glacier parameters including surface area, equilibrium line altitude (ELA), and accumulation area ratio (AAR) by combining a digitized historical topographic map, Landsat images, and a USGS DEM. The results of the remote sensing analysis demonstrate significant downwasting and loss of mass at the margins of the glacier and areas of the glacier that are bounded by bedrock. In-situ monitoring has chronically underestimated this downwasting. This study quantified a substantial up glacier migration of the ELA and a corresponding reduction in AAR. Comparison of the AAR associated with each Landsat scene to the established equilibrium AAR for the Taku Glacier indicated that the Taku glacier has transitioned from a long period of positive mass balance to relative equilibrium. This transition likely presages a new period of retreat for the Taku glacier, which will have widespread consequences for downstream ecosystems and economies.

Two subspecies of Indo-Pacific lionfish, (Pterois volitans and Pterois miles Family Scorpaenidae) are the first invasive marine fish that are spreading rapidly throughout the Western Atlantic and Caribbean waters (Schofield 2010, S117-S122). The lionfish were introduced to Florida waters in the mid 1980's; however, little is known about the lionfish population and the long-term effects this non-native carnivore will have on our native reefs. Despite increased awareness among wildlife and marine professionals, the public remains in the dark about the ever-growing lionfish populations in the on Western Atlantic reefs. Through a case study of the Story Map model this project utilizes the spatial data revealing chronological factors of the lionfish population growth and spread throughout the entire Western Atlantic. This study is designed for informing the lay citizen and inspiring them to act. The Story Map model is a public interactive web platform, or portal into the story of the lionfish. Using a Story Map, this project seeks to raise public awareness of the rising populations of lionfish in Florida coastal waters. The primary goal of this thesis is to inspire individuals through the Story Map to get involved their coastal communities. The Story Map combines interactive maps and high definition media to communicate the lionfish population growth as well as how the lionfish have affected the communities. This effective use of a Story Map is highly adaptable and the purpose can change as the focus of the invasive lionfish changes too. This Story Map model is simple, highly flexible, and can rely exclusively on public data. This thesis is a call to the public to increase awareness and involvement to aid our native Atlantic and Caribbean reefs against this invasive species.

Sidescan sonar coupled with a Global Navigation Satellite System (GNSS) provides a near photographic image of features underwater for use in mapping applications. Sidescan sonar acoustic pings are drawn as raw images, spatially referenced using GNSS coordinates and then mosaiced in specialized software to produce coverage areas. The resulting two dimensional images can then be analyzed in Geographic Information Systems (GIS) for manmade or natural underwater features. This technology has a special application for mapping eelgrass extent in Southern California, which has become a focus of research for the National Oceanic and Atmospheric Agency (NOAA) and several National Marine Estuary programs. Eelgrass is a submerged aquatic flowering plant that provides critical structural environments for resident bay and estuarine species, including abundant fish and invertebrates and is often a primary diet for several grazing snails. This project demonstrates the viability of creating a low-cost Unmanned Surface Vehicle (USV) with an attached sidescan sonar sensor for scientist and researchers to use in mapping eelgrass. The remote sensing imagery collected by the USV is classified in ArcGIS to calculate the full spatial extent of the visible eelgrass beds. The results of this project show acoustic imagery collected by a USV can be used to create classified bottom composition maps through manual classification. Unsupervised classification did not produce the desired results and is still a work in progress. By demonstrating these mapping tools, researchers can conduct studies at a much lower cost and on their own time instead of relying on expensive, contracted surveys.

With an estimated 3,862,210 people currently residing in Los Angeles, this city is the second most populous metropolis in the United States. Like most major cities in the nation, Los Angeles faces an affordable housing crisis. Given the challenge, local government officials seek the best practices that will ensure that residents at all income levels have access to fair, safe, and affordable rental housing. However, existing land use and zoning regulations and location and availability of qualifying site amenities make it difficult for the City to achieve this goal. This research investigates suitable sites for the construction of higher density affordable rental housing developments (55-218 dwelling units/acre) in the City of Los Angeles. It identifies and examines factors such as land use, zoning, cost of land, fair share, employment, and site amenities meant to maximize the effectiveness of affordable rental housing developments?defined as providing housing to very low-income, low-income, and moderateincome households. Furthermore, it explores how these variables limit the policymakers' abilities to move forward with these types of projects. Accordingly, a fair share analysis, service area analysis, and site suitability analysis of Los Angeles are performed to identify suitable sites for the construction of higher density affordable rental housing developments. The site suitability analysis consists of six iterations that simulate the different perspectives that play a role in the production of higher density affordable rental housing developments. Results of the analysis indicate that existing land use and zoning regulations and the established criteria for qualifying California Tax Credit Allocation Committee (CTCAC) site amenities impact the production and location of these types of developments in the City of Los Angeles. The weighted linear combination method is applied to this analysis to show how Geographic Information Systems (GIS) technology and techniques best support housing policy.

Throughout the United States, aging water infrastructure creates continuous challenges for safety and water quality. Maintaining infrastructure takes considerable organization and coordination. Hydrants are critical for maintaining high water quality and for safety-precautions such as firefighting and dust control in construction. This thesis project aims to determine what factors contribute to hydrant damages in Buffalo, NY through the use of spatial analysis with geographic information science (GIScience) and asset management. It is hypothesized that, due to weather patterns specific to north and south Buffalo, there will be significantly more hydrant damages reported for south Buffalo. Additionally, more hydrants will be damaged during severe winter weather in the locations where snow accumulation is greatest. This study utilized data on hydrants and corresponding hydrant maintenance and weather data at multiple scales to test these hypotheses. Hydrants and corresponding damages were analyzed based on spatial location and temporal (seasonal) scale. Hot spot analysis was used to determine areas where significant clusters of hydrants are located and where maintenance as a result of vehicle damage may be statistically significant. Hydrant failure and repair data were analyzed based on the frequency of occurrences each day, and in relation to weather patterns. In an additional analysis, weather data were analyzed on days when severe storms occurred, to determine if more hydrant repairs result from severe weather. As predicted, south Buffalo reported a greater rate of damaged hydrants than north Buffalo; however, contrary to the second hypothesis, hydrant damages were not consistently confined to areas of Buffalo with the greatest snow accumulation. Understanding how location and seasonal weather factors cause hydrant damage or increase maintenance will help Buffalo to identify highly susceptible areas. Buffalo can use the results from the analysis to strategically implement preventative maintenance and save city funds.

Wildfire is a growing problem in the United States that lends itself well to spatial analysis for those seeking to minimize human and environmental damages. This thesis analyzed spatiotemporal trends of wildfire in the state of Florida between the years of 1985-2014 and analyzed ecological and human demographic variables in relation to wildfire ignitions. Human population numbers, population growth, precipitation, and temperature affect the spatial distribution of wildfire. These changes can modify fire regimes in many areas, though the direction and extent of this influence is not fully understood. This research used correlation analysis to study the components of wildfire ignitions, separated into human and natural caused fires, visualized fire locations, and examined fire ignition hot spots in relation to the causes. It is hypothesized that population growth and population numbers positively influence the number of human caused wildfire ignitions, while high temperatures and low precipitation increase lightning caused fires. To create spatio-temporal maps and conduct the analysis, data on wildfire points, population counts, precipitation, and temperature were gathered and analyzed. Spatial analysis (e.g., Hot Spot Analysis (Getis-Ord Gi* statistic)) and non-spatial statistics (e.g., Pearson's correlation) were used to analyze statistically significant clustering of wildfire incidence. This thesis also used historical data to better recognize trends in wildfire occurrence and distribution. Wildfire management groups, already dealing with large fires every year, can use this information to become better prepared for future changes in wildfire incidences. The analysis revealed no significant correlations between the study variables and wildfire incidence. However, the research did reveal that there is significant clustering of wildfire ignitions due to human activity and lightning strikes.

The purpose of this thesis project is to empower San Diego County, California residents by educating them on the potential wildfire risk to their homes by providing local hazard and preparedness information through a publically accessible, web-based geospatial application. Wildfires are uncontrolled vegetation fires that directly threaten the homes, populations, and livelihoods of all Southern Californian residents. Exponential population growth in California has expanded home constructions into more rural geographies, which increase the frequency of wildfires in the region. The growth of the Wildland-Urban Interface (WUI), areas where new home construction meets vegetation fuel, has fed the frequency and scale of destruction incurred by manmade and spontaneous wildfire occurrences in San Diego County. Whether wildfires are caused by nature or influenced by people, the unpredictable nature of these hazards means they can easily spread to populated areas and present a reoccurring threat to San Diego County communities. The focus of this research is the development of a web map mashup that incorporates well-documented, published spatial data such as the wildfire hazard potential and fire hazard severity zones in the San Diego region into easily understood features on a publicly accessible web map. San Diego County homeowners may utilize the web map mashup to understand the relative risk of a wildfire to their home, the history of local wildfire burns, their proximity to emergency response resources, and real-time wildfire information. This study utilized Esri's ArcGIS desktop to prepare the data, ArcGIS Online to publish and host the data, and ArcGIS Web AppBuilder to produce a custom map template with analytical widgets and tools. Lastly, professional and public review of the web map mashup was solicited and incorporated into the final version of the web map application

Cycling as a form of urban transportation has been growing in popularity across the United States over the past several years. While many cities have added protected bike lanes in recent years, the City of Philadelphia does not have a single protected bike lane, despite the fact that among American cities with one million or more people, Philadelphia has the highest share of commuters who bike to work. Due to the unprotected nature of cycling infrastructure in Philadelphia, bike lanes are routinely blocked by motor vehicles, presenting a significant safety hazard to cyclists. Previous efforts to raise awareness of blocked bike lanes - including a campaign by the Philadelphia Parking Authority encouraging cyclists to tweet the location and photographic evidence of blocked lanes to the #UnblockBikeLanes Twitter hashtag - have been ineffective. Therefore, this project aims to create a more robust method for documentation of blocked bike lanes in Philadelphia, through use of an Android app that provides a spatial representation of blocked bike lane occurrences. The app, named Philly Bike Report (PBR), utilizes a cloud database to allow users to view and report recently blocked bike lanes. In addition to the core focus on collection and display of volunteered geographic information on cycling conditions, PBR also allows users to contribute to the #UnblockBikeLanes Twitter campaign by providing the option to tweet the incident upon submission. By creating a mobile app and accompanying cloud database of blocked bike lanes, PBR aims to provide a more effective method for viewing and reporting blocked bike lanes in Philadelphia. The key findings of this thesis are represented by the creation of PBR, as a demonstration of how a mobile app with a cloud database can be used to view and report blocked bike lanes.

Since the 19th century, excavations at the Maya site of Tikal have continually provided intriguing archaeological insights into the Maya world. Tikal was one of the most influential powers in the Southern Maya Lowlands, and maintained wide-ranging relationships with neighboring sites throughout the Maya area. These inter-site relationships are described extensively in the epigraphic record of Tikal and its neighbors. As one of the major powers in the Maya region, Tikal was engaged in frequent warfare with rival cities in the Lowlands. The objective of this thesis project was to model probable paths for Tikal's warfare interactions in the region through the use of least cost path analysis. The study generated three separate sets of points, to the Maya sites of Caracol, Calakmul, and Naranjo using the Path Distance tool in ArcGIS. Least cost path analysis expresses the efficiency of each route as a function of time and distance. The study generated these least cost paths through the use of Tobler's Hiking function in order to express how the inhabitants of Tikal would have traveled through the unique terrain of the Maya Lowlands. This thesis also used sensitivity analysis to test the location sensitivity of the modeled paths. These analyses determined that the least cost paths diverge significantly if input data is altered. The least cost path analysis indicated that the modeled routes represented a set of probable paths from Tikal to its neighboring rival sites.

Interest in marsh birds has increased in recent years due to their role as indicator species of wetland health, which is exacerbated by their declining numbers. Marsh birds are secretive, hiding in thick marsh vegetation and infrequently emitting sound, making it hard to locate their habitat and determine their distribution and numbers. Previous studies to monitor marsh birds have been conducted to determine effective conservation and management methods. The North American Marsh Bird Monitoring Program (NAMBMP) estimates changes in breeding marsh bird abundance at different temporal and spatial scales across the country. Consistent with this approach, a pilot program, including a survey sampling scheme, database, and mobile application was developed using biological and environmental data specific to the state of Maine. This was achieved using the Esri Catalog of GIS Applications and the Blue Marble Geographics GIS Application: Global Mapper and projection management tool: the Geographic Calculator. Biogeographical data were captured, stored, and analyzed. A two-stage cluster sampling approach was used to identify potential breeding habitat for secretive marsh birds from which sites to survey were identified. These data were converted via taxonomies and unit conversions to correlate to the regional and national scale standards of the NAMBMP. Twenty of the survey sites were selected and field surveys were conducted to verify the accuracy of the points. In 2018, the Maine Department of Inland Fisheries and Wildlife (MDIFW) will use the resulting database and mobile application to complete the Maine section of the NAMBMP.

Community Water Systems (CWS) are public or private water systems that supply water to the same population year-round, and they provide drinking water to over 300 million people in the United States each year. While the majority of the population is served by larger CWSs, where populations range from 10,000 to 100,000+, there are three times as many small water systems, where populations are less than 10,000, and these water systems generally have smaller budgets for maintaining or improving their systems. Many of these small CWSs rely on outdated maps or have no maps at all, and may not have the budget for a GIS. The goal of this study is to develop inexpensive geospatial solutions that can be implemented in small rural CWSs. As a case study, a geographic information system (GIS) was created and implemented at the Descanso Community Water District in San Diego County, California using open source GIS software. The Descanso CWD relied on outdated paper CAD maps and has no spatial data of their infrastructure. Locations of all features found in the water system were collected using high accuracy GPS. The locations of pipes were digitized with the aid of Descanso CWD employees using Esri's ArcMap. The datasets were added in QGIS, an open source GIS software, and displayed over aerial photographs and other background data. A detailed map atlas of the water system and a small-scale wall map were created. Metadata for all data was created in XML format. A system employee was trained on the functions of the GIS software that are necessary to update the data as needed, which will allow the Descanso CWD to operate and maintain their GIS in-house instead of relying on a contractor to perform the work for them. A workflow for implementing a GIS at a small CWS was then created based on the findings of the case study.

This project offers a comparative study between a ground-based weather station and satellite-based method of calculating evapotranspiration (ET). Using four selected cloudless days measured between 10:28 am and 11:30 am DST local in Los Angeles in 2016 (08 February, 19 April, 22 June, and 08 July), the aim of this project is to determine if the Normalized Difference Vegetation Index (NDVI) Triangle method, a satellite-based methodology, could be used as an acceptable alternative to measure ET for locations without a ground-based weather station. The Moulton Niguel Water District (MNWD), located in Orange County, California was selected as the study area for this project. Weather station #245, located on the Coto De Caza golf course, generated the ground-based weather data for the Penman-Monteith calculation of evapotranspiration for the MNWD. In contrast, the satellite method used the Landsat Data Continuity Mission's (LDCM) multispectral and thermal bands to calculate ET by Land Surface Temperature (LST) and NDVI values. These two data inputs, by the application of band math, were combined to create a two-dimensional NDVI Triangle pixel plot evapotranspiration estimate for each day calculated. The data from the two methodologies were then compared, with the assumption the Penman-Monteith method was the most accurate measure of ET. The results were statistically compared and analyzed for accuracy through Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE) and the coefficient of determination calculations. The results, although limited by sample size, indicate the NDVI Triangle method can be used to estimate ET. The process of creating the NDVI Triangle 2-D plot from multispectral and thermal bands from the LDCM is presented in detail, in addition to the process of defining the dry and wet edges of the plotted NDVI Triangle.

The Bering Sea, Aleutian Islands, and Gulf of Alaska yield one of the largest sustainable fishing industries in the world. To ensure continued sustainable practices, the effects of fishing activity on the health of the ecosystem should be studied actively. Bottom-trawl gear is a sustainability concern because it directly interacts with the benthic layer. Impact from bottom-trawl fisheries is difficult to assess, particularly over the long-term. Using fishery-dependent observer data from National Marine Fisheries (NMFS) provides insight on the location and the intensity of fishing effort, which can identify areas most exposed to fishing pressure. In this study, the spatial and temporal extent of Alaskan bottom-trawl fishing effort in the Bering Sea, Aleutian Islands, and Gulf of Alaska as defined by NMFS data collected between 1993 and 2015 was explored in a space-time cube in ArcGIS Pro v1.4.1. The variables analyzed were number of hauls per area and total catch per area. Statistical techniques were used to examine spatiotemporal autocorrelation and clustering in these data. Results indicate that fishing effort was nonrandomly clustered over space and time (Moran's I, and exact result and probability). A threedimensional hot spot analysis shows which areas were most intensely fished and illustrates the long-term trends over the study period. The data were then compared with two external factors, sea ice concentration and closed marine protected areas, to determine the effect of changing regulations and climate on fishing activity. The analysis uses long-term retrospective data to examine changes in fishing effort over time in the Alaskan bottom-trawl fishery. The implementation of new MPA's in previously fished areas caused a shift in fishing effort to the still open border areas. Sea Ice had a limited effect on fishing effort spatial patterns, but certain areas in the Bering Sea exhibited increased fishing effort in years with less sea ice effect.

This thesis details the creation of an open-source Web GIS application that can quickly match crime incidents and location-tagged social media posts that share location in space and time. The objective was in support of an over-arching goal of providing a publicly available investigative tool that could be used by citizens and members of law enforcement, augment existing social media strategies in law enforcement, and aid in accelerating case clearances. Detailed herein, are the methods for, and the open-source and free-to-use technologies used to, create the application. While the application works as expected, it was discovered that the study area did not produce the amount of location-tagged social media information needed for a consistently high rate of spatiotemporal co-location of crime incidents with social media posts. Analysis of the data collected for this work confirmed the reasons for the lack of co-locations and revealed directions for future work, which are also discussed.

The Sun Link Modern Streetcar route opened on July 25, 2014, in Downtown Tucson and on the University of Arizona campus. Even though the opening of this 3.9-mile route has been hailed as a success due to its role in revitalizing Downtown Tucson, there are still major issues with accessibility to mass transit in greater Tucson. In this study, mass transit accessibility in Tucson is measured using a GIS-based transit accessibility model before and after the opening of the Sun Link system to determine the effect the streetcar had on overall transit accessibility in the Tucson Metropolitan Area. The study is focused on the years 2009 and 2014 (i.e., two points in time, five years apart) to clearly identify accessibility differences before and after the Sun Link system began operation. The analysis compares transit routes and resulting access for each residential parcel in Tucson to a diverse set of land uses based on the Land Use Public Transportation Accessibility Index (LUPTAI) during the study interval. The study finds that while residential parcel accessibility increased on average, accessibility to a diverse mix of land uses at transit stops themselves decreased on average within the study interval. Recommendations from this analysis are important in determining what areas of Tucson need improvement on mass transit accessibility. This thesis serves as a demonstration of the effects of a new rail transit system in medium-sized metropolitan areas and provides the first implementation of the LUPTAI GIS transit accessibility model in the U.S.

Turf replacement rebate programs are a water-conservation measure promoted by many local and regional government agencies in California. In an effort to reduce outdoor water use, these programs offer financial incentives to homeowners who replace water-intensive lawns with drought-tolerant landscaping and more efficient irrigation systems. Previous studies, however, have found that landscaping choices are based on more than just economic factors; social pressure, neighborhood norms, and property value are also important considerations, and homeowners tend to opt for landscaping similar to that of their neighbors. This study uses GIS, linear regression, binary logistic regression, and a comparison of means to characterize a spatiotemporal spillover effect in turfgrass replacement rebate program participation data for Long Beach, California. The study determines that residents are more likely to participate in a Lawn-to-Garden program when one or more neighbors on the same block have already completed turfgrass replacement projects. In fact, a block with a single project completion is 5.8 times more likely to see a future application submission than blocks where no projects have been completed, and the highest future application rates occur on blocks where more than 8% of households have already completed a Lawn-to-Garden project. Project completions on adjacent blocks were found to be far less influential. These findings indicate that residents are more willing to replace their conventional landscaping with drought-tolerant gardens after an alternative norm has been established on visually adjacent properties, suggesting that local governments should consider focusing their turf replacement program marketing and support efforts on blocks with no prior participation

Maps provide information to researchers, as each map represents the environment from the perspective of the creator, at a set point in time. Libraries act as sources of information for researchers and can provide resources for users to locate, request, and receive maps. However, each library has differing capacities for users to access maps, and that capacity can even differ within the same organization. The United States (U.S.) Marine Corps is an example of an organization that has libraries with differing levels of accessibility for users accessing maps. At this time, remote users do not have access to hard, or paper maps, in the Library of the Marine Corps located in Quantico, Virginia. Therefore the aim of this thesis is to design an innovative process for remote users to access hard maps from the Library of the Marine Corps using digital components. To design a suitable process and spatial database, this project focuses on how users currently search and access hard maps and digital components from the Library of the Marine Corps, what the metadata standards for geospatial data and cartography currently consist of, common access capabilities of other libraries, and current practices for scanning, georeferencing, and extracting vector features from hard maps. A detailed entity-relationship model illustrates an efficient spatial database that can accompany the process, revealing the relationships between different spatial objects involved in users locating, requesting, and receiving digitized versions of hard maps and associated digital components from the Library of the Marine Corps. Finally, this project evaluates the spatial database and process for integrity and suitability for the Library of the Marine Corps. Other libraries housing important historical as well as current collections of hard maps could use the results of this research.

In early 2015, a surge of vineyard applications alerted Los Angeles County to a potentially largescale habitat change in the Santa Monica Mountains North Area. The County took action by placing a ban on all applications until an ordinance could be written to protect the natural environment of the mountains. Then in December 2015, the Los Angeles County Board of Supervisors adopted a vineyard ordinance regulating the presence of vineyards in the Santa Monica Mountains North Area. The ordinance limits the size of vineyards and creates a set of regulations for landowners to follow. Several of these regulations have spatial manifestations that can be analyzed through the use of GIS. This project sought to visualize these regulations spatially and to evaluate their effectiveness in protecting the habitats of the Santa Monica Mountains North Area. This project evaluated the ordinance's effectiveness in three ways: (1) by determining the amount of land protected by the ordinance as a whole; (2) by quantifying the impact of each regulation spatially and understanding how they compare to each other; and (3) through a statistical evaluation of the amount of vegetation saved due to the ordinance. The ordinance was effective in achieving its goal of preserving the natural habitat of the North Area by protecting 16,223 acres, which is an 88% drop in potential habitat change. The ordinance also protects nearly all vegetation classes by 50% or more, with 13 of them being fully protected. The natural habitat of the Santa Monica Mountains North Area is a rare and fragile Mediterranean ecosystem that should be closely monitored by its residents and local government. This project gives the community a quantifiable representation of the ordinance and its effects.

Earthquakes have produced losses of over $60 billion since 1971. Of these, California has suffered the highest losses nationally. These losses include building and bridge damage, destruction of building contents and business interruption. The risk factors (as they pertain to loss from earthquake damage) are large stocks of old buildings and bridges; many high-tech and hazardous materials facilities; extensive sewer, water, and natural gas pipelines; earth dams; petroleum pipelines; other critical facilities; and private property. The secondary earthquake hazards (which include liquefaction, ground shaking, amplification, and earthquake-induced landslides) can be just as devastating as the earthquake itself. Damage caused by an earthquake depends on the quality of the buildings' construction, the density of the area, the pattern of intense shaking, and many other factors. Should an earthquake occur in a densely populated area with older buildings, loss of life and damage to infrastructure would be much higher. This study performs and evaluates two potential earthquake scenarios for the City of Downey utilizing the Federal Emergency Management Agency (FEMA) HAZards U.S., Multi-Hazard (HAZUS) Earthquake Model. According to the Downey General Plan, there is a 50% probability that a major earthquake will occur within the next 30 years along the Whittier-Elsinore Fault, which is 40 miles northeast of Downey. In addition, the Anaheim, Puente Hills, Elysian and Newport-Inglewood Faults are within or near Downey's city limit and those faults are all active or potentially active faults. For this reason, the Whittier and Puente Hills faults with Magnitude (M) 6.8 and 7.0 respectively were chosen to run in the scenarios. HAZUS, which runs on an ArcGIS platform, along with Comprehensive Data Management System (CDMS) was used to ingest updated data, model the earthquakes and create output maps. Essential Facilities data were updated via data provided from the City of Downey Water Work Department into the CDMS. United States Geological Survey (USGS) ShakeMaps were ingested via the

This thesis discusses the author's inception and development of the Chigoe Flea Eradication Project (CFEP) and Tungiasis eLibrary web mapping applications, created to raise awareness about and actively combat tungiasis, a disease of poverty caused by the microscopic flea Tunga penetrans. The CFEP was designed to illustrate the efficacy of web GIS as a disease management strategy by establishing a collaborative virtual workspace for aid workers, aid organizations, and governments of afflicted regions. The apps empower the movement of epidemiological data from the local scale to the global scale using volunteered geographic information (VGI). Community aid workers can use the CFEP to track the provision of field surgeries, shoes, and medicine, to record patient demographic data, and to document the use of pesticides in sleeping shelters and communal areas during visits to stricken villages. At a regional scale, aid groups involved in tungiasis prevention and education are invited to provide contact information and delineate service boundaries on a map using the CFEP interface. The second app, the Tungiasis eLibrary, was developed in response to recent changes in World Health Organization (WHO) policy creating a pathway to assign new diseases to the neglected tropical disease (NTD) classification, which introduces greater opportunities for awareness and funding. As part of a reclassification request, WHO member states or regions are invited to submit a petition including a profile of the disease and its distribution. The Tungiasis eLibrary, a collection of georeferenced scientific literature pertaining to the disease, was designed with the intent to serve as that profile for tungiasis. The eLibrary app is populated by VGI in the form of scientific literature, white papers, and data contributed to the eLibrary by researchers and activists. Additionally, the collected articles are displayed on a global map, developing the firstever authoritative global spatial distribution of tungiasis.

Hiking trails are subject to change over time due to several factors, manmade and natural. Changes in trail condition pose a danger to unprepared hikers. Collecting data about trail conditions in rough and remote terrain can be difficult and expensive. Gathering information through volunteers can be a cheap and effective alternative. In this project, hiking trail locations and features were obtained from the City of Santa Barbara. A web application and website were developed as a proof of concept to show users existing data for hiking trails in Santa Barbara and to collect volunteered geographic information (VGI) about trail conditions from users onsite. Three different analysis methodologies were performed on the VGI to determine its validity. A positional analysis of the features submitted presented clustering of the VGI towards the original feature point, a thematic analysis revealed a general consensus on the condition of a feature, and a comparison of the VGI data against control points using photographs taken with a mobile device exhibited variances in the distance from the original feature. The VGI data was then modified into the same format as the City of Santa Barbara's dataset to demonstrate how it could be prepared for submission to the city. The results of this project show that a website and web application can be used to collect VGI on hiking trails. The VGI data collected can be used to update the City of Santa Barbara's hiking trails dataset.

Mapping surface mines and mine activity is integral to both environmental preservation and tracking industrial production. West Virginia has a long history of coal and other types of surface mining, but detailed spatial information representing the extent of operations is limited. In developing nations, such information often does not exist in any form. While field collections of spatial feature data relating to mine activity are costly and resource-intensive, remotely sensed imagery presents a readily available tool to identify and map surface mines and their footprints on the Earth's surface. Geographically referenced raster image datasets from sensors on board satellites in space as well as airborne vehicles can represent wavelengths of light well beyond the range of the visible spectrum. These types of multispectral datasets present grids of cells on the Earth's surface that each represent the luminance properties of the surface materials at clearly defined wavelengths of light. This study analyzed recently collected multispectral data from West Virginia by examining the reflectance values at each point on the ground and attempted to classify materials known to exist in high concentrations in large mounds or pilings that are typically adjacent to large-scale surface mines. By inputting the known spectral properties of these Earth minerals into the classification process, this study was able to automatically classify and map the signature features of surface mines without user input or analysis. The automated classification methodology developed and tested in this study accurately identified surface mine locations throughout the study area in West Virginia at a rate of over 98%, and the output feature dataset can be implemented immediately in a comprehensive impact study of mining operations on the surrounding environment and populations.

With advancements in GIS technology and computer capabilities there has been an increased interest in species distribution modeling (SDM). Previous works have focused on creating SDMs to determine presence while many ignore how the environment interacts with the species abundance levels. This study attempted to determine the most suitable method for predicting spatial distribution as well as the abundance of several different fish species in Lake Ontario. Ten fish species that were observed in Lake Ontario benthic trawling surveys at least 5% of the time between 1978 - 2014 were used to develop models. Subsets of the original dataset were also used to account for periods of time that saw major changes in Lake Ontario. This included a dataset before the invasion of dreissenid mussels, a dataset after the invasion of dreissenid mussels, and a dataset for the years limited to when Round Goby (Neogobius melanostomus) occurred within the trawling surveys. Generalized Linear Models (GLM), Generalized Additive Models (GAM), and Geographically Weighted Regression (GWR) were compared to each other to determine the best method. Habitat variables used to determine abundance relationships consisted of depth, fetch, fishing depth temperature, distance to major rivers and wetlands, as well as the presence of other fish species in the trawl. Adjusted R 2 and Cohen's Kappa were the primary indicators for determining the best method. None of the methods were able to produce good models with the habitat and biological data used. GWR did show an improvement in overall modeling performance, based on this study's criteria, over GLM and GAM. This was done by producing adjusted R 2 and Cohen's Kappa values similar to the GAM models while using a less complex regression model by using fewer predictive variables.

Numerous studies within the last 200 years have shed light on the socioeconomic patterns of the Beaker culture during the Bronze Age, particularly in the United Kingdom. However, with the expanding role of GIS in the field of archaeology, there is an increasing amount of spatial data on this cultural group, allowing opportunities for analysis that can begin to describe inter- and intrasite spatial connections. The geographic connections of pathways, for example, can illustrate the corridors of cultural exchange that gave rise to and sustained the Beakers for over 1,000 years. Using Least Cost Path analysis, this thesis aimed to model such spatial connections in Northeast England. The study generated 66 anisotropic LCPs that modeled possible path connections between sites. The first 18 LCPs served as the primary LCPs between sites - within clusters and between clusters. Three assessment tests were conducted to validate these LCPs. First, for each primary LCP, another LCP was generated traveling in the reverse direction. Second, new segments that utilized pairs of nearby sites, approximating the alignment of original pairs, were generated; the new segments also included a primary and a reverse LCP. Finally, areas with high LCP coincidence were compared to aerial images for coincidence with paths or features. The study found that the LCPs were mostly coincident or near coincident. However, varying degrees of local variation in the trajectories of many LCPs were evident. Four areas with high LCP coincidence or near coincidence were selected for aerial imagery comparison which showed LCPs generally following watercourses. Generating LCPs can model human movement during the Bronze Age; however, datasets that describe the environmental conditions of the period as well cultural datasets that spatially delineate territories and taboos are needed in order to more xi accurately understand the efficacy of these LCPs and the costs associated with prehistoric travel in the region.

Urban centers continue to densify and increase in number as the world's population grows. Landslides are a common hazard throughout the world and can cause significant loss of life and property. Landslide risk and damage to the built environment is often an outcome of urbanization, whereas in the natural environment damage due to landslides is considered part of nature taking its course. This study examines the most common landslide triggering variable, precipitation, in Western Washington State, a region prone to this geohazard. The methodology developed in this study utilizes freely available, currently cached and real-time soil, geology, land use, demographic, and weather data provided by state and federal agencies and required no field research. It is imperative in high-risk landslide zones to have easy access to accurate landslide prediction models available in an open format. Integrating real-time data into validated landslide risk and relative risk assessment models through a geographic information system (GIS) can increase the utility, accuracy, and ease of use of a given model. The model developed in this study reports potential risk to urban and rural environments as well as risk to specific demographics for a specified landslide event. Landslide triggering variables are well suited for real-time streaming due to their continuously changing behavior. By publishing and publically sharing the model as a web service thus making it available on via the internet, the methodology also encourages collegial and professional discussion. Thus, this study provides an example of data integration of traditional landslide risk assessment variables with real-time precipitation into a landslide risk and relative risk model that can be readily adapted to investigations into landslide hazards in other locations.

Healthcare accessibility for veterans are services that are physically accessible, available, and acceptable to the eligible population. This research study examines spatial and non-spatial relationships to assess accessibility of primary care for military veterans. Centered on a Veterans Integrated Service Network, the study begins by developing catchment areas for Veterans Healthcare Administration facilities and using a two-step floating catchment area (2SFCA) model widely adopted in healthcare studies, scores accessibility for populations by census tract. It expands on previous research by focusing on veteran care and modifying the Enhanced 2SFCA (E2SFCA) methodology through the application of impedance factors based on non-spatial measurements, including appointment wait-times and patient satisfaction. These modifications address the requirements of the Veterans Access, Choice, and Accountability Act of 2014 as well as explore analysis based on concepts of acceptability of care. The result is the designation of areas that fall short of delivering primary care services within the context of federal legislation and also a relative scoring of the degree of accessibility to care in areas that meet the federal requirements. The methodology in this paper provides the flexibility for application in different studies and geographic regions, and the results provide information that may prove useful to policy-makers.

The goal of this project is to develop an Ethington Transection Toolbox (ETT) to automate, increase the efficiency, and improve the efficacy of creating "Ethington Transections." Ethington presents these hybrid charts/maps to visualize social change in space and time, along urban streets in his forthcoming book, Ghost Metropolis: A Global History of Los Angeles since 13,000. A new technique for visualizing the act of moving through the landscape over time, "Ethington Transections" are defined as a cross-sectional sample of data from polygons to simulate a single, directional line of transit. The objective of this thesis is to streamline the creation of transections resulting from the input of common polygon-distributed data, and to share such a tool so that others may benefit from increased efficiencies. The final result is a custom toolbox in Esri's ArcGIS ModelBuilder of seven custom models with contextual help, written documentation and video walkthroughs. This series of models creates an editable map and graph layout and an organized geodatabase of intermediate outputs that can be reused for additional analyses or presentations. The ETT shortens the time to complete an "Ethington Transection" from 8 hours to slightly less than 1 hour. The previously tedious and time intensive task of creating transections was automated and made accessible to a wider range of researchers, facilitating new perspectives and interpretations of data. Therefore this toolbox should enhance the analytic skills of those looking to study how changes occur through space and time along any linear sample of data to simulate a transit in polygonal datasets.

Drones are unmanned aerial vehicles that are remotely controlled. They range in size from under one pound to several hundred pounds (Perlman 2016). This thesis addresses drones classified for consumer use, which the Federal Aviation Administration (FAA) defines as drones between 0.55 to 55 lbs. (FAA 2016e). Since consumer drones have been available for purchase in greater numbers than ever before, legislation related to no-fly zones needs to be centrally organized (Perlman 2016). This can be done through the creation of a geodatabase and web-GIS map, which will allow for visualization of drone use areas. The study area for this thesis is the state of Maryland, which was chosen because it contains every type of FAA no-fly zone and has not passed any drone use sub-national rules; this allows for the current FAA regulations to be studied and improvements reccommended where necessary. This web-GIS map will allow state government policymakers, drone hobbyists, and other members of the public to see where it is appropriate to use drones in Maryland. Visualizing and making drone use data universally available will reduce accidental drone trespassing and will guide users to where drone fly-zones areas are located. To achieve this goal, a geodatabase was designed with five feature classes to show the required data and steps necessary to catalogue and display drone use data properly. A web-GIS map was then constructed that allows users to differentiate between types of fly zones and obtain details regarding the permissibility of drone flight in these zones. This geodatabase, coupled with the web-GIS map of appropriate and inappropriate drone use fly-zones, provides an effective model for other states to use to create their own drone use maps.

Annual fieldwork at the Bethsaida, Israel archaeological excavation project yields an unwieldy amount of data that have historically been processed and managed via paper-based means and have no associated spatial data. There has been little adoption of modern technology applications to manage this data, even in recent years. The programming objective of this project involved designing and implementing an intra-site, archaeological specific, spatial database for collecting and managing excavation artifacts. A project-based approach was taken toward improved digital data management, tracking, mapping, and visualization in the examination of temporal and spatial archaeological data, thus facilitating the ability for archaeologists to gain new and otherwise undetected insights through spatial pattern analysis. Legacy data, along with data collected via a handheld Global Position System (GPS) device in 2015, aided in establishing the dataset parameters, feature classes, attributes, and domains of the database. This excavation site offered a unique opportunity to explore the space-time continuum through numerous human settlements evidenced by the vertical archaeological record representing the 10th century before Common Era (BCE) through the 1st century Common Era (CE). Visualization of the distribution, concentrations, and spatial relationships of material culture to settlement groups potentially illustrates social trends and cultural practices over the centuries. Data recording will become more consistent and efficient through structured, predefined categories and attributes, bringing greater organization via ontological and semantical consistency. Field collection will be further streamlined and enhanced by the adoption of handheld devices working congruently as an extension of the new geodatabase, collecting artifact information and spatial data, including stratification, in real time. Ongoing research and global collaborative opportunities become possible with the geodatabase, and greater cohesion amongst the diverse excavation team is vii enhanced. Archaeologists are further able to forecast areas for future excavation based on the visualizations.

Since the turn of the twenty-first century, concerns with disparities in food access and food consumption have been a growing topic for scholars and activists alike (Reisig and Hobbiss 2000; Whelan et al. 2002). The incorporation of agriculture in urban settings is one possible remedy to sustain population growth and increasingly high demands for food. Green spaces can help high-risk communities gain access to fresh, organic produce and reduce the presence of food deserts. However, within the spectrum of sustainability socioeconomic factors play a critical role in a community's access to healthy organic foods. Although various studies associate an increase in access to food with the implementation of urban agricultural practices (LeClair and Aksan 2014), social exclusion remains a dominant obstacle in the successful integration of Urban Agriculture (henceforth: UA) in communities facing food insecurities (Meenar and Hoover 2012; Tiarachristie 2013). By expanding on the research and data collected by CultivateLA, this study assesses the relationship between clusters of different types of UA practices in LA County based on income levels to determine possible overlaps with food deserts in underserved communities. Using the geospatial analysis methods of Hot Spot Analysis, Buffers, and Directional Distribution to test the bivariate hypotheses, the pattern demonstrated by each of these phenomena, UA sites and food deserts, reveals that there is a significant statistical difference between them based on income levels within LA County. The findings indicate that a higher number of UA sites are located in neighborhoods with low percentages living under poverty, while 85% of neighborhoods with high percentages living below poverty are designed as food deserts. These results provide spatial statistical evidence of how these phenomena overlap, providing a platform for further exploration by city planners and other policy makers to remedy limited access to healthy foods in high-risk areas.

Earthquakes, which were previously rare events in the Dallas-Fort Worth (DFW) Metroplex, have become increasingly common in the last five years. In a five-month period, Irving alone had 26 earthquakes over magnitude 2.0. Damage has been minimal, but larger earthquakes have not been ruled out as new fault lines have been discovered and their precise structure is only beginning to be understood. This project's goal was to show how HAZUS can be used to demonstrate possible earthquake effects on the new fault lines, and how the results from HAZUS can be used to assess the impact of damaged bridges on the road network and shelter accessibility. Two fault lines discovered in the last couple of years were digitized and loaded into the HAZUS model. Historical earthquake data were used to form a hypothetical earthquake scenario that characterizes local conditions. The scenario was run twice, once on each fault. To explore how these results can inform emergency planning, output from the HAZUS scenarios regarding the amount of people needing shelter per census tract, as well as damage to bridges and their location, was imported into ArcMap. A road network was built to support a locationallocation model to assign people needing shelter to potential shelters and the damaged bridges were added as barriers. A centroid was calculated for each census tract to provide population source points. Lacking data on the location of existing emergency shelters, all schools throughout the two counties were designated as potential shelter destinations. Then location-allocation analysis was conducted on each county's data to determine the closest accessible shelters with available capacity. The demonstration scenario resulted in not enough shelters, as several source points were unallocated. It is hoped that the demonstrations provided by this study will encourage city planners to begin to address emergency planning in the region for these newly identified hazards.

The population of the State of Washington is growing rapidly, especially in areas surrounding Seattle and Tacoma. The population in 2010 was reported as 6.7 million and is projected to be 9.9 million by 2060, an anticipated growth rate of approximately 50%. This population growth leads to increased development in the suburbs of major cities and towns, causing urban sprawl. Washington State is also home to seven active volcanoes, all within 100 miles of major cities. As urban sprawl occurs, development extends into areas adjacent to volcanoes. Due to these trends it is important to understand the location and size of future development of the region for decisionmaking and hazard mitigation. This study focused on the region surrounding Mount Rainier, as it is the volcano closest to Seattle and Tacoma. A land use change analysis must be performed to assess how urban development could be impacted by volcanic hazards. This study uses the Land Use Conflict Identification Strategy (LUCIS) model created by Carr and Zwick (2007) to visualize potential land use in conflict with volcanic hazards. Potential future allocation of conservation, agriculture, and urban land use was determined using economic, transportation, physical geography, agricultural, and biological data. Results show that urban land is most suitable in areas near existing urban areas in the western portion of the study area. Agriculture lands are most suitable through the central portion of the study area and conservation land is suitable in the majority of the study area. Future land allocated to urban land exceeds the number of acres required to sustain the future population, by pushing into the agriculture land while conserving more lands suitable for conservation. Urban cells affected by a volcanic eruption of Mount Rainier have the potential to double with new development. This study creates a visualization of where developers can plan for the future while limiting the impact of volcanic hazards on humans and their property.

Commercial, government and private use of Unmanned Aerial Systems (UAS) are rapidly expanding in the United States. Although commercial use of UAS is still limited to a case by case basis, the Federal Aviation Administration began allowing companies to petition for use of UAS for commercial purposes. As of October 30th, 2015, 2020 exemptions have been granted to companies in various industries. Those companies approved to use UAS for surveying see a need for the technology, but must also weigh the capabilities and limitations of UAS to acquire and process survey data against those of more traditional methods. This study sought to answer the question of whether or not using UAS for topographic mapping and volumetric surveying can lower the cost and time to complete the same task using land surveying and manned aircraft systems while still achieving acceptable accurate results. This study compares the use of UAS within the surveying and mapping industry with traditional and accepted methods and provides a comparison of their use. Specifically, this thesis reports on tests comparing UAS data acquisition and processing for volumetric calculation and topographic mapping. Time, accuracy, and cost were compared between UAS and traditional survey methods. The results of this study showed that using UAS for topographic mapping and calculating volumes is more time and cost efficient than land surveying, with no loss in accuracy, but only when performed over bare earth terrain. The results also showed UAS to be more time and cost effective than using terrestrial Light Detection and Ranging (LiDAR), but with less accurate results. The author is currently employed as the Flight Operations Manager for a large surveying and mapping firm, and the position involves the day-to-day remote acquisition of survey data through the use of aerial LiDAR and aerial photography, as well as the establishment of a UAS department within the 6 company. In addition, flight of all kinds, both manned and unmanned, has been a passion of the author since becoming an aviator in the United States Army in 2004.

Precision agriculture in practice utilizes GIS far less effectively than it should. My work at a soil consulting company has shown that part of the problem is that the literature does not show an effective way of analyzing soil through GIS that is both scientific and able to be used by those associated with agriculture. The thesis aimed to answer two questions: (1) Are there significant differences between a multiple year composite yield and a single year and if so, are these significant enough to have an impact on normal operations? (2) Are the areas where such differences occur related to slope? Six fields were used for this study: Dob Along G, Emmert, Harstad, Merkt’s, Pribnows, and Stefoneks located near New Richmond, Wisconsin. Three years of yield data were used for each field and slope data was created using LiDAR from St. Croix County. These yield data were interpolated using standard industry practices. A single year was compared to composite years to determine what differences, if any, exist between them. Each year and the composite had actual and predicted values compared using the difference of means statistical test to validate the success of the interpolation. A DEM was created from LiDAR and this was used to create a slope map of each field. This slope map was used to divide the yield points by five slope classes: 0 -1°, 1-2°, 2-3°, 3-5°, and >5°. The mean yield and variation was then compared for each class to determine any patterns associated with slope values. The results show that where there is significant variation between single years of yield data the composite will fail. The difference between the composite and a single year is useful in identifying which fields are causing the composite to fail and eliminating them. Slope did not consistently correlate to changes in yield or variation. Dob Along G, Emmert, and Harstad showed no correlation, while Merkt’s, Pribnows, and Stefoneks showed decreasing yield and increasing variation as slope increased.

Asset management systems can save organizations time and money by enabling staff access to well-organized and easily retrievable information. Visualizing the physical and contextual locations of these assets in a geospatial application increases the understanding and efficiency of staff. Often times, Geographic Information Specialist (GIS) analysts create and maintain asset information using specialized software programs, however these software platforms are often not user-friendly to non-GIS practitioners. Consequently, comprehension and adoption of GIS technologies requires special training and hands-on experience. The benefits of managing this information in GIS may not be realized if others cannot access the data. This thesis presents two easy-to-use GIS web applications developed for non-GIS staff at the VA Palo Alto campus to visualize and better understand the geospatial context and data of their 93-acre campus facility. The applications focus on irrigation infrastructure and include: irrigation controllers, back flow valves, gate valves, and all of their respective areas. Users can quickly locate shut off locations of irrigation pipelines when an immediate need arises such as a line break or a required maintenance activity. The applications developed for this thesis provide a template for managing other utility assets through web applications for the VA Palo Alto campus.

The expansion of low-density residential development into wildlands places a variety of stresses on Earth's natural systems. Urban sprawl is one of the many factors contributing to water shortages, higher resource costs, and increasingly destructive storms, all of which fit under the umbrella of global climate change (Gencer 2013). In California, it is wildfires that capture the headlines during the summer and fall months. With wildfires becoming a common occurrence within the California landscape, it is crucial that local government and agencies work to create proactive approaches to mitigate the severity of these events. The areas of chief concern occur where structures and infrastructure are intermixed or adjacent to wildland fuels; this area is known as the wildland-urban interface (WUI). Mapping the WUI is a useful resource for assessing which communities and developed areas are most vulnerable to wildfire. County cadastral data was used to assess the spatial extent of the WUI within Lassen County, California. This method is a large improvement to past studies because it eliminates the need to estimate the areas where housing is located. California Department of Forest and Fire Protection (CAL FIRE) Fire Hazard Severity Zone Maps were then overlaid to identify the areas in most need of emergency planning and preventative action. Results show that in 2015, the spatial extent of Lassen County's WUI was 1,016.67 sq. km. (392.54 sq. mi) and accounted for 101,666.68 hectares (251, 222.83 acres) or 8.32% of the study area. The WUI area included 5,456 residential structures (49.51% of the total housing), which was a 13.22% increase and a 1.89 percentage point increase from 2000. 81% of the WUI occurs on private lands.

Previous studies of park accessibility have utilized network analysis and dasymetric mapping to investigate pedestrian accessibility to park resources measured in acres per capita. Through a case study of Downey, California, this study extends on previous work in this area by combining network analysis and dasymetric mapping with robust park amenity auditing. The intention of this study is to provide a more detailed examination of how accessibility is affected by park condition and the types of facilities provided to park users. The study uses a method for dasymetrically mapping population data to land parcels, Esri's ArcGIS 10.1 Service Area Network Analyst tool, and a park amenity scoring system based on the Physical Activity Resource Assessment (PARA) instrument. The results of this research reveal that park accessibility in Downey is limited at multiple Service Area (SA) distance levels due to the presence of parks with high pedestrian accessibility but low amenities in the geographic center of the city and parks with low pedestrian accessibility but high amenities on the city's periphery. The results of this case study inform policy suggestions for future park developments. These policy suggestions include planning strategies for increasing pedestrian access to parks with developed amenities, which are distant from residential areas. Also, the study indicates which parks to nominate for development in highly accessible areas with few amenities.

Plug-in electric vehicles (EVs) have shown benefits in reducing gasoline consumption. One of the key domains affecting EV penetration in the U.S. market is the EV charging station infrastructure. Charging equipment varies by charging time, how much a battery holds, types of batteries, and the types of Electric Vehicle Supply Equipment (EVSE). The charging time can range from 15 minutes to 20 hours depending on the above variables (Alternative Fuels Data Center 2015). The most affordable EVs on the U.S. auto market, excluding the Tesla, are able to cover approximately 70?80 miles on a full charge (Schaal 2015). The average range of electric vehicles per charge is much less than that of conventional gasoline vehicles. Currently, the problem is that there are not enough public charging stations to supply the increasing number of electric vehicles on the road. The goal of this thesis is to determine where to install EV charging stations at public facilities of Los Angeles County. The data used in this study are based on existing public facilities of Los Angeles, such as government offices and public libraries and parks. This analysis section is divided into three sub-sections: DC Fast Charging Infrastructure, Public Access Charging, and Workplace Charging. The three approaches are explained in the Methodology section and the results are discussed in the Results section. This study demonstrates how site suitability analysis based on geographic information system (GIS) data can provide information useful for installing public EV charging stations in Los Angeles County. The findings of this study show that, by applying the site suitability method, Los Angeles County would be able to install more EV charging stations at optimal locations and to serve the needs of their intended users.

Every year thousands of people become lost or injured to the extent that a search and rescue (SAR) unit needs to step in and help. Through the ages, we have needed to look for people and things yet the theory behind searching goes back less than 75 years to World War II. The main idea is that to be successful, searchers need to search the right area, and be able to detect the person or thing. This research explored the utility of using a GIS-based mobility model to assist search planners in developing their search areas. A mobility model incorporates consideration of the speed with which a person can move across the landscape. The tool used here is an Esri ArcGIS template called Integrated Geospatial Tools for Search and Rescue (IGT4SAR). While it includes many SAR tools, this research focused on the mobility analysis component. This study specifically assessed IGT4SAR's ease of use, speed, and success rate at determining how far a person can travel in a given time. Nevada County provided detailed information on a few incidents used to gain familiarization with IGT4SAR and the state of Oregon provided a large database of historical and diverse SAR events that allowed for broader testing of the model. Ultimately, 44 incidents were used to test the model. The model itself is easy to use, but the template is complex. With preloaded data, the model creates a product in less than 15 minutes. Starting with an unrealistic assumption that the incident start time recorded in the database represented the time when the subject left the last known location, test runs resulted in a 30% success rate where the found location fell in a time band that was less than the amount of time between the start time and the found time recorded in the database. After adding a estimated three-hour delay in reporting time to the SAR notification times the model had a 75% success rate. These results suggest that IGT4SAR can assist in defining a containment area to limit a search radius and is worthy of continued development.

AB 109 (Public Safety Realignment) widely changed the way criminal offenders are processed in California, starting 1 October 2011. It is widely purported that AB 109 is affecting crime rates in the State of California. This paper studies the spatial effects of AB 109 on crime rates in San Diego County. Studies have shown Criminal Offenders will likely commit offenses near their place of residence. Recidivism is a complex and serious problem in California, the United States and the World. Regression and hot spot analysis as well as traditional statistics methods were used to analyze crime rates, or crime events per 1000 persons at the census tract level. Five categories of crime were studied: AB 109 categorized offenses or offenses falling under the AB 109 statute, Non AB 109 offenses, Crimes Against Persons, Crimes Against Property and Crimes Against Society. Analyses indicated that crime rates for most categories studied decreased. Property crime rates exhibited a median increase of 0.7 events per 1000 persons at the census tract level. Spatial OLS analysis indicated a correlation between residence locations of AB 109 offenders and a hot spot of property crime rate increase however the model was misspecified. Other category hot spots exhibited no correlation with AB 109 offenders. Variance of the crimes against persons hot spot was explained by different variables. Some other combination of complex variables not listed or tested as part of this study is responsible for the variance of the hot spots of other categories. The implementation of AB 109 appears to have been successful in that offenders are being diverted to County facilities and reducing the State prison populations and is associated with several categories of crime rate decrease in San Diego County. However property crime has exhibited a statistically significant increase in crime rates across San Diego County coinciding with AB 109. However no significant correlation was found between populations of AB 109 offenders and crime rate increase of any categories.

According to the routine activity theory, violent crime may be deterred by a capable guardian. Cohen and Felson's routine activity theory asserts three conditions need to be met for a crime to take place: a likely offender; a suitable target; and the absence of a capable guardian (Cohen and Felson 1979). A hot spot analysis of violent crimes for Washington, DC shows a divided city. In northwest DC, the census block groups correlate with low violent crime rates. To understand why northwest DC has low crime rates, a quantitative spatial analysis uses housing characteristics as proxies for capable guardianship to test whether a correlation exists between capable guardianship and the deterrence of violent crime. The rationale behind using housing and homeowner characteristics in a model relies upon fusing capability with perception of success. Accordingly, if the criminal perceives a capable guardian to be present, then the criminal will not commit the crime. Following this logic, neighborhoods displaying capable guardianship through housing characteristics ought to have lower violent crime rates. Using exploratory regression, Ordinary Least Squares, and Geographically Weighted Regression the construction of a guardianship model with significant explanatory variables suggests a relationship between capable guardianship and areas with lower violent crime rates do exist. Furthermore, quantitative spatial analysis suggests a strong relationship between low violent crime rates and obtaining higher levels of education exists.

Citizen scientists and governments are managing resources and collaborating at a level unimaginable before the era of Web 2.0. The opportunities that currently exist for these collaborations are as exciting as they are manifold. These opportunities are only out-paced by the numerous challenges that scientists and citizens face. This thesis presents web mapping applications and associated databases developed for collecting, storing and depicting groundwater well data. These Geographic Information Systems (GIS) tools were developed to support the collaboration between citizen scientists and a local water agency, monitoring groundwater resources in two basins in Sonoma County, California: Santa Rosa Plain and the Sonoma Valley Groundwater Basin. The government-supplied data provided by the Sonoma County Water Agency and volunteer gathered data provided by volunteers from Sonoma Valley ensured the continued success of groundwater resource monitoring. While these volunteers were not trained professionals, the data and information that they provide is unique and invaluable. Their efforts, coupled with the local government agency have made resource management plans for groundwater successful. This thesis was successful in improving the previous methods of data management, communication and data-sharing while also providing opportunities for future improvement.

Sinkholes are naturally occurring geologic phenomena which form when karst erosion causes the surface to collapse. Karst formations can be found globally as a result of water eroding soluble bedrock which creates features such as fissures, caves, and sinkholes. In the United States, every state except Rhode Island has the presence of karst terrain and, therefore, the potential of developing sinkholes. Sinkhole formation can negatively impact society, manifesting mostly as property damage, and in some tragic cases, causing a loss of life. There is a lack of protocols for tracking and recording sinkhole events data nationally. The sinkhole inventories that are available do not include all sinkhole activity and are primarily found among different State Geological Surveys (SGS) databases. The objective of this thesis was to create a single unified geodatabase (UG) schema based on existing SGS sinkhole databases. The majority of SGS sinkhole data is in the public domain and is of an authoritative source while only two states are utilizing Volunteered Geographic Information (VGI). Two states, in particular, Florida and Kentucky, influenced the geodatabase design because of their developed structure and relative completeness respectively. The proposed UG combines authoritative and VGI elements from multiple databases. It is composed of two feature classes and three tables that are joined by primary and foreign keys. Additional design elements stem from database design theory and sinkhole research studies. The geodatabase design was tested by implementing a prototype database for a portion of Florida. The design was evaluated against the needs of three potential user communities: geologists, insurance fraud investigators, and the general public. Based on these fundamentals, a single UG template was created that can be implemented at the SGS level, and lay the foundations for a national geodatabase in the future.

The city of Hilo, Hawaii is more vulnerable to tsunamis than any other location in the United States. Due to the unique bathymetry, topography, and location relative to the Cascadia Subduction Zone, in the future, Hilo could be struck by a large tsunami similar to the historic 1946 and 1960 events. The Cascadia Subduction Zone can produce a 9.5 M earthquake with the potential of generating a tsunami with maximum wave heights of over 29 feet. Before devastating economic loss occurs, it is imperative that such potential flood inundation and consequent dollar exposure are understood. This study compares the Joint Research Centre's (JRC) Scenarios for Hazard-induced Emergencies Management (SCHEMA) flood model implemented using ArcGIS with the Federal Emergency Management Agency's (FEMA) Hazards-United States (HAZUS) flood model to simulate the potential impact of a large-scale tsunami on the city of Hilo. The SCHEMA and HAZUS models, the National Oceanic and Atmospheric Administration (NOAA), and the State of Hawaii provided the spatial data required to build the financial and structural inventory database for these analyses. Field measurements recorded during the 1946 and 1960 tsunamis and corresponding historical inundation maps provided input into the models. The results of this research suggest that although the SCHEMA model has the benefit of being more customizable, the HAZUS inundation scenario can be implemented with fewer input data and produce results comparable to historical damages. Future work will involve refining the inundation scenarios to include more detailed input data such as historical terrain (digital elevation models), field-verified updates to the structural inventory database, and an increased number of predicted events based on wave height.

With the proliferation of online GIS starting around 2012, costs for running GIS have come down so much that there are now many opportunities to spatially enable organizations like those in the non-profit sector that could not access the technology before. This research demonstrates how to administer a simple GIS system for a non-profit corporation in the performing arts sector, the Pacific Symphony. It illustrates how the symphony developed a basic pattern of spatial thinking and analysis that strategically aligned with their core organizational objectives. This project shows that even though the symphony lack the resources to invest in a professional GIS system, they were still able to utilize spatial technology by implementing a cloud-based GIS system to make their organization more successful. Esri's ArcGIS Online was used for this project because it is a cloud-based, user-friendly GIS software geared to those with little to no GIS experience. By overlaying the symphony's data with ArcGIS Online content, such as demographic data and tapestry segmentation, ArcGIS Online was able to help the symphony choose ideal locations to market and select among alternative performance venues. Additionally, it helped the symphony reduce costs by targeting the appropriate market and customer base. Two key findings coming out of this project are the importance of a GIS champion within the organization to make the GIS implementation successful, and the value of hands-on experience of Web GIS for integrating patterns of spatial thinking in the organization.

Spatial scientists spent the better part of the last three decades pushing for further integration of Geographic Information Science (GIS) technologies in K - 12 curriculums. Their efforts to date are leading to moderate breakthroughs in geography and physical sciences, but social studies continue to neglect its use almost entirely. Unfortunately, little empirical evidence exists that suggests students realize quantifiable gains from its inclusion in the classroom. In fact, the findings from most research comparing visualization methods indicate that static mapping methods outperform dynamic methods when assessed by the user's ability to extract information from the product. This study adds to existing literature by expanding upon current research into static versus dynamic visualization methods. In contrast to previous visualization studies that focus heavily on animation for their dynamic representations, this study tested static methods against story maps to determine whether they provide teachers an advantage in the classroom. To develop its findings, the study employed standard classroom instruction methods and examination materials to identify which visualization method most effectively communicated the material to students in secondary school history classrooms. The study divided students into a control group using standard classroom static visualization tools, and an experimental group using dynamic story maps. Written exams conducted immediately following initial instruction, and again two weeks later, provided the basis for evaluation. The study failed to demonstrate that dynamic products provide students a distinct advantage over traditional static products in a classroom environment. Its findings suggest that students can use both tools equally effectively, supporting the findings from previous research. Of note, this study suggests that among female students, dynamic products may yield decreased learning outcomes. This indicates the need for further research to identify how gender affects visualization strategies.

An analysis of urban morphology was conducted in the San Francisco Bay Area using Local Indicators of Spatial Association ("LISA") to quantify clusters of different types of Urban Amenities (Anselin 1995). Concentrations of different types of Urban Amenities were given a centrality score, which was then used to delimit the Social Center or Centers of the Bay Area. This thesis project used Samuel Krueger's (2012) methodology, employing multiple regular hexagonal arrays of different size to aggregate indicator amenity points. The aggregated clusters of amenities were calculated, assigned cluster scores, and ultimately ranked by centrality and finally shared as a cartographic visualization. Previous methods for delimiting urban structure focused on employment centers, commuting patterns, and the Central Business District ("CBD"). This research seeks to expand on Samuel Krueger's method measuring clusters of Urban Amenities that describe the experience of place to delimit an ambiguously bounded but internally consistent central place known as the postmodern urban center (Krueger 2012; Dear & Flusty 1998). The objective was to determine whether San Francisco represents the center of the San Francisco Bay Area, or if the nickname the "Bay Area" better fits the region today. This study reveals both polycentrism and strong centers with two dominant urban centers: San Francisco and Oakland-Berkeley; and an unexpected suburban center focused on the Silicon Valley, capturing Santa Clara and Sunnyvale, but mostly excluding San Jose.

Flooding in the U.S. annually accounts for almost $8 billion of property damages and social impact, prompting the need for insurance, aid, mitigation and other programs which rely on predictive flood damage modeling. The Federal Emergency Management Agency (FEMA) developed the HAZUS FL (FL) model to support these programs. FL creates estimates based on descriptions of people and property, known as the general building stock (GBS), which detail the number and types of buildings within each census block group (CBG). The accuracy of flood damage models is dependent on the relationship between the locations of the GBS and floodwaters. To ensure that FL remains relevant to a wide audience, techniques are needed to enhance the accuracy of these factors in the FL model which do not require additional detailed building datasets or alter the existing FL software code. Improving the GBS representation by applying dasymetry to the GBS would improve the accuracy of the FL model estimates. This thesis demonstrates the viability of dasymetric GBS by applying land use/land cover data to align the GBS with developed land to improve the accuracy of FL models. These effects are most pronounced in areas with partial flooding and/or low density development. CBGs experiencing severe flooding or high density development displayed limited damage differences compared to the current FL building format.

This study demonstrates the integration of Geographic Information System (GIS) with asset management. Asset management can be explained in many ways - for example, an organized infrastructure management system, an economic approach to help planning and decision-making, a methodology to ensure the future level of service life of a facility. It is a systematic process of maintaining, upgrading and operating physical assets cost effectively. It provides the tools necessary to facilitate a more organized, logical approach to decision-making and implementing. It gives a comprehensive view of resource allocation and utilization. Asset management strategies and systems used by the facilities management, construction, and information technology industries provide a framework - a multidisciplinary approach that enables us to consider whether such systems might be able to organize better the data for GIS applications. The aim of this thesis is to investigate the potential of an asset management and GIS approach to organize and manage the data. This thesis demonstrates that using a Microsoft Excel spreadsheet was not appropriate for holding all the data and running an accurate asset management system. This thesis investigates the requirements for a geographic data-focused asset management system and makes a recommendation to the administration on using the MPet.Net system to provide County of Kauai, Department of Parks & Recreation (COK DOPR) with the capability to identify all of the COK DOPR's assets and track all maintenance associated with the assets. This thesis shows that the system will then provide the means to support and determine budgets and future long-term expenditures thru the extensive database of information.

The Southern California region faces the constant threat of earthquakes due to the hundreds of faults that lie just beneath this region's surface. As earthquake prediction technology is limited, it is important that residents, including students at the University of Southern California, are prepared for an earthquake event. This project develops and assesses the impact of an interactive web-based Geographic Information Systems (GIS) application, titled USC Earthquake, as an educational tool for communicating information about earthquake preparedness on the University of Southern California University Park Campus. This study incorporated previously conducted research regarding the use of GIS as a tool for emergency preparation, the implementation and assessment of educational programs for emergency preparation, and the description of other earthquake-related mapping applications. The application created for this project included data from the USC Department of Fire Safety and Emergency Management and the Los Angeles County GIS Data Portal to communicate information about the location of emergency supplies and assembly areas on campus. The author processed this data using Esri's ArcMap as well as ArcGIS Server and constructed the application using ArcGIS Web AppBuilder. This study assessed the educational impact of this tool by surveying two groups of undergraduate student participants: an experimental group, who were asked to use the application, and a control group, who were asked to view a stationary map. The data collected for this survey ultimately showed that both map visualizations are useful in communicating information about earthquake preparedness. However, analysis of the results demonstrated that users preferred the static map to the interactive visualization. The thesis concludes by providing recommendations regarding the use of this application as well as concerning future studies similar to this.

Los Angeles County (LAC) is the most populous county in the United States and is simultaneously vulnerable to numerous natural disasters, particularly earthquakes. LAC is situated on ~68 active seismic zones and studies suggest that LAC is expected to experience a 7.8 magnitude earthquake on the southern section of the San Andreas Fault sometime in the next 30 years. Consequently, it is critical to understand the extent of damage LAC could face from a large earthquake. This study analyzed potential damage to non-ductile concrete buildings in LA City Districts 1, 9, 10, 13, and 14 should a repeat 1933 Long Beach earthquake occur on the Newport-Inglewood fault. The Hazards United States Multi-Hazard (HAZUS-MH) program was used to analyze the impact of a Mw 6.4 earthquake, assess the amount of damage, identify areas of vulnerability, and examine economic impacts of a repeat event. The Mw 6.4 event was run three times to show how HAZUS-MH results improve when the model includes updated datasets. The first analysis used built-in HAZUS-MH data, the second incorporated independent datasets, and the third deployed the Advanced Engineering Building Model (AEBM). Pre-1976 non-ductile concrete building data was added to the AEBM to evaluate the damage to individual structures. This study furthered the work done by Comerio and Anagnos (2012) by utilizing the AEBM to evaluate which of the concrete buildings are the most vulnerable.

Site suitability modeling in geographic information systems has not been previously used to gauge the strength of historic structures as preservation candidates. The goal of this project was to develop an ArcGIS model and related methodology to serve as a screening process when evaluating a large number of potentially eligible structures. While an automated method cannot truly replace an evaluation by an expert, it can serve to make the process of evaluating a structure more efficient. This model can be used to streamline the evaluation process, and save time and resources by removing from consideration those structures that are obviously unsuitable, and ranking the remaining candidates based on various criteria. An expert can then make the final evaluations. As a case study by which to develop and test the modeled evaluation process, structures on the Main Garrison area of Fort Ord were evaluated using the model. Fort Ord is a former United States Army post north of Monterey, California. Closed in 1994, it contains a large number of structures dating from between 1940 and the late 1980s. Despite Fort Ord's significant role in US military history throughout much of the 20th Century, there are currently no plans to preserve any of the structures on the base. Confirming the validity of the proposed model workflow, Fort Ord buildings identified in an a priori assessment of building significance scored highly in the model. These results suggest the model workflow can become a useful addition to the cultural resource management toolkit. Additionally, this framework can also potentially be useful as historic communities evolve and develop, in determining which structures to preserve.

This work evaluates and reports the accuracy assessment of Maximum Likelihood Supervised Classification (MLSC) using the different stages of Mountain Pine Beetle (MPB) infestations outside the Lake Tahoe Basin Management Unit (LTBMU) using Landsat 8 OLI 30m and WorldView-02 (WV02) 2m (comparatively higher) spatial resolution imagery. Using ArcGIS 10.3, the accuracy of satellite imagery using MLSC and the Enhanced Wetness Difference Index (EWDI) provide a good comparison of the imagery at dissimilar spatial resolutions. MPB infestations at epidemic population levels can cause economic losses and have detrimental effects ecologically in Lodgepole pine tree stands. Detecting endemic populations of MPB can prevent epidemic infestations, preventing economic and ecological losses. After pre-processing, using the different stages of the MPB infestations as a control points, MLSC and the calculation of Tasseled Cap Transformation (TCT) indices (e.g., to calculate EWDI) are used to assess the accuracy of each type of imagery. The overall accuracy results of MLSC of Landsat 8 OLI 30m (51.22%) supersede those of WV02 imagery (26.82%) and are shown in error matrices within this thesis. Accomplishments of this project include the advantage to using WV02 imagery to locate MPB infestations at their endemic stage rather than relying on annual ADS's. Improvements in positional accuracy of Global Positioning System (GPS) data collection devices and improved Remote Sensing (RS) software for image analysis may improve this analysis.

The contemporary urban environment is often thought to be an area comprised of fixed objects with clear and identifiable boundaries. However, static interpretations of space overlook the fact that urban environments are susceptible to cyclical temporal variations, especially when it comes to healthy food availability. This study tests the claim that time promotes inequitable effects in access to healthy food. The study examines pedestrian food access in an urban environment using network analysis of roads and dasymetric mapping based on cadastral data to illustrate food access fluctuations. It also provides a detailed assessment of walking access to healthy food retailers for individual parcels at different periods during a single year. The application of the methods in this study provides a realistic assessment of food access in an urban environment so that developers and retailers have a better understanding of where retail spaces, farmers markets, and community gardens are needed. The assessment maps show lower access to healthy food during winter months due to seasonal closures of farmers markets and produce stands in addition to decreased harvest yields in community gardens. The results indicate that the portion of Everett's population that has 0.5-mile access to healthy food retailers during the peak of seasonal retail is 63% and drops to 52% during the fall and winter. Overall, the differences in access were significant. However, vulnerable neighborhoods were affected less than others. The findings suggest that alternative factors other than time could be more useful when determining a population's access to healthy food such as income disparity and transportation availability. This indicates the need for further research to identify more suitable ways of gauging access, and that temporal variations may not be as valuable in a food access study compared to others geospatial studies.

As groundwater is a vital resource, it is important that oil and gas operations do not jeopardize water quality. Many consumers, including farmers and municipalities, rely year after year on the freshwater provided by aquifers. Along South Texas, oil and gas companies are targeting the Eagle Ford formation containing hydrocarbons. In this same region, the Carrizo-Wilcox aquifer must be drilled through to reach the Eagle Ford below. To protect the above aquifer, cemented surface casing is used to seal the Carrizo-Wilcox from contaminants within the well borehole. This study incorporated Geographic Information Systems (GIS) to evaluate surface casing depths of oil and gas wells, to verify if they are deep enough to adequately protect the aquifer. To understand the geologic structure occurring in this region, aquifer depths obtained from well logs were used to interpolate the base of the Carrizo Sands. After comparing three interpolation methods, the Empirical Bayesian Kriging (EBK) interpolator, using the Exponential Detrended semivariogram, was selected to create a predicted surface and a standard error map. Surface casing depths of Eagle Ford wells were mapped and queried to determine if they are deeper or shallower than the predicted surface representing the aquifer. Over half of the wells within the study area had surface casing shallower than the aquifer. However, most of those fell within areas where groundwater was brackish. Results from this study should motivate regulatory agencies in tightening up policies and guidelines pertaining to oil and gas operations affecting aquifers within the State of Texas. In addition, methodologies conducted during the study provide a viable means to improve the current process of determining surface casing depths.

This research involves a habitat model of sperm whale (Physeter macrocephalus) distribution utilizing Passive Acoustic Monitoring (PAM) within the central Gulf of Alaska. The main goal of this project was to explore the relationship between distinct occurrences of sperm whale vocalizations and environmental variables within a 144,560 km2 Temporary Maritime Activities Area (TMAA) during the Summer of 2013 (Rone et al., 2014). A total of 6,304 km of trackline was utilized to produce 426 hours of 'standard' real-time monitoring to detect vocally active cetaceans. Acoustic activity, along with nine static geophysical and dynamic oceanographic variables were used to produce empirical statistical models in order to express correlations and spatially represent their probable habitat range. The application of customized GIS-based components has allowed the performance of iterative geoprocessing, and a precision-based spatial approach to habitat distribution modeling. Various Generalized Additive Models (GAMs) were developed with discrete trackline acoustic encounters and combinations of habitat variables to offer a comparison of encounter rate differences across the study area, as well as demonstrate the habitat variables' ability to predict sperm whale presence. Modeling efforts indicated that the most important explanatory variables for sperm whale habitat within the spatial and temporal scale of this study were depth, slope, proximity to the 2,000 m isobath, sea surface temperature, chlorophyll-a concentration, and magnitude of oceanic currents. This work demonstrated that acoustically detected sperm whales found within the central Gulf of Alaska follow predictable foraging patterns and demonstrate consistent preferences for specific oceanographic conditions.

Prehistoric sites and artifacts are common across the country side in the high elevation desert of California's northeastern corner. For decades archaeologists have been researching, surveying and cataloging archeological sites on lands managed by the Bureau of Land Management (BLM). While thousands of sites have been recorded, it is hard to say how many remain undiscovered. Multiple archaeological site prediction models have been completed covering the area to assist archaeologists in locating and recording sites. This project tests the hypothesis that the site type Maxent model can be as good or a better predictor of archaeological site probability than the Maxent models that do not categorize by site type. The site type Maxent model will also be as good or a better predictor of archaeological sites than the previous models at a project scale. To test this hypothesis three models were run (1) the "kitchen sink", all 3,729 sites within the study area, (2) ecological region, using all sites categorized by the ecological region in which they fall, and (3) site type, a subset of 1,332 sites, categorized by the prehistoric people use at that site. Maxent uses the spatial location of individual archaeological sites and environmental variable rasters to produce a probability of distribution raster. At the study area scale the Maxent software's built-in validation tools, environmental variable performance and Area Under the Receiver Operator Curve (AOC) the three Maxent models were compared and to test the hypothesis. At a project scale a 5,800 km2 archaeological survey area was used to compare how well the Maxent models and the previous models were able to predict recorded site locations. This project was unable to definitively prove the hypothesis; however the results show that the site type Maxent method of modeling provides a successful method for predicting archaeology site locations at the study area and project scales, with some additional work being needed.

Cities throughout the country are constantly striving to improve their perceived image. Whether it is requiring lush landscaping in commercial developments, or simply making sure that the trim on a house is properly painted, cities are constantly struggling to get citizens to comply with municipal codes. Such is the case in the City of Victorville, CA, where economic recovery has been slow following the 2008 housing market crash, leaving poorly maintained properties in its wake. Presently, Victorville's code enforcement staff is doing a proactive enforcement survey of all single-family homes in the city in an effort to "clean up" these properties. However, the survey is inefficient and is taking up a good amount of officer time, leaving commercial and industrial areas of the city neglected. This project was able to predict which houses in Victorville are likely to have a code enforcement violation that requires action from staff in order to better allocate resources to areas that require more attention and pull resources from areas that do not require attention. The primary question here is what property attributes can be used to predict the occurrence of a code enforcement violation? Several have been selected, including property value, length of ownership, and presence of a previous violation. A binary logistic regression analysis was run on three areas of the city containing approximately 2,200 homes that have already been surveyed in order to train a model for predicting the remaining 29,000 homes. Geographically weighted logistic regression was then employed to factor in spatial variation in the relationships between the response variable and the explanatory variables. The success of this model will make Victorville's code enforcement more efficient, and it is a model that any city can employ to make its own code enforcement departments more effective.

Many U.S. cities are expanding passenger rail transit to improve transportation system performance. This is particularly the case in Los Angeles and has presented specific challenges for Los Angeles Metro's rail maintenance, safety, and external emergency services personnel. Rail maintenance personnel require familiarity with the location of stations and a variety of rail equipment (crossing gates, power supply, signals, etc.) for routine maintenance and especially during emergency incidents. A common question asked by Metro field personnel is "Where is the equipment item located?" Typically, answering this question would require prior field knowledge, engineering drawings, or computer systems, none of which may be available in an emergency. However, as part of a solution, a paper-based, pocket size Rail Equipment Locator Map (RELM) was developed for Metro's Traction Power department. While these maps proved useful, the development process presented challenges involving quality assurance, mapping and data management. The goal of this study was to establish a viable development, production and maintenance methodology that would improve quality, require less development time while fitting Metro's current hardware/software environment. Combining Esri desktop geographic information system (GIS) tools including ArcGIS, ArcCatalog, ArcPy, MS Access local database management software, MS PowerPoint, and MS Visual Basic for Applications with partial automation, required less time to update the new RELM test product, while achieving format consistency, improved spatial accuracy, and reduced risk of errors. In the final analysis, the new methodology demonstrated a significant time based benefit-to-cost ratio improvement and should result in greater rail operations efficiency.

The purpose of this thesis is to demonstrate how thematic mapping and GIS technology can enhance genealogical research for family history records in the United States of America from the 17th to the 21st century, using online resources from FamilySearch.org, the Newberry Library, and Google. Many genealogists have not had significant exposure to GIS and web GIS. The research focuses on the potential to infuse dynamic web GIS technology with online family history resources in a single application. To this end, an electronic, dynamic, and interactive map was designed that illustrates a more complete picture of family history with regard to data quality, cartography, and history than existing web resources provide. As a proof of concept for the potential to integrate web GIS and genealogy four electronic maps were developed on behalf of four ancestral lineage records. The maps were embedded in Google Earth's user interface and distributed to users. The GIS application map is a conglomerate of several hundred maps that can be manipulated in different ways. The maps are animated and controlled with inputs making them dynamic and interactive. Users were invited to take part in a survey regarding their experience with the application. User feedback indicated positive results regarding the impact of GIS infused genealogical activities with measurable increases in user knowledge and interest. Program successes indicate the potential for future development in the online web-enabled environment.

This project provides a series of four paths for light rail between the Downtown Central Hub and the Northwest Metropolitan Transit Authority of Harris County (METRO) Transit Center in Houston, Texas. These potential light rail routes are alternatives to a failed proposal for University and Uptown routes that would still connect Houston's downtown central business district (CBD) with its largest financial district, the Uptown District. Building such an additional line would have an immediate, positive impact by promoting traffic efficiency, health benefits, environmental renewal, business growth, and commuting options. The method used builds upon prior least-cost path (LCP) research by incorporating domain specific engineering standards, lessons learned from the prior failed proposal, and viable path corridors (VPC). The results of the study are paths that follow existing light rail, freight rail, and road rights-of-way (ROW). The results include four iterations of the model: VPC only, Population, Residential Roads, and All Cost Rasters runs. Based on lessons learned from the prior DOT and METRO light rail line study and the application of the combined VPC and LPC method, the study found several feasible route options that run in areas different than the failed Uptown and University routes. These alternatives are suggested as preliminary candidate routes that will later be refined by planners, engineers, and surveyors.

Since the mid-nineteenth century, excavations in southern and central Turkey have provided a bounty of archaeological records. One such excavation at the ancient site of Kultepe features preserved, clay tablets that document economic transactions that took place in the mid-to-late Bronze Age. Additional records further allude to the means and routes of transporting goods from the Amuq Valley in the south, across the Amanus Mountains to the Cilician Plain. The research objectives of this study were to utilize a Least Cost Analysis across the Amanus Mountains to map potential routes of these merchants. The study generated individual Least Cost Paths for seven sets of points using the ArcGIS Pro Path Distance tool. Five of these points pairs were used for mapping potential backways whereas two sets were used to model existing passes. The five paths identified three significant passageways across the mountains. Despite shared corridors among these routes, the modeled paths illuminated several passes across the mountains that were possibly utilized in Bronze Age merchant travel. The modeled routes were tested against the existing routes as an indicator of validity for the results. The least cost path geographies and travel time estimates demonstrated stable results.

Spatial data is used in many industries and it is common for large organizations to internally manage and employ spatial data to assist with operations. The intent of this thesis is to identify and accommodate existing technical architecture strategies, user requirements, and platform software in order to design an enterprise data management solution specific to raster spatial data used within an international oil and gas exploration organization. Company specific data management objectives are identified and assessed in terms of their viability with respect to data acquisition, management, and dissemination. Raster data model improvements were considered necessary to provide web and third party applications with centrally hosted data. Raster technology innovations are examined within the context of enterprise data management practices, providing use case examples that leverage database raster management technology. High and low-level components for the design of the enterprise data management solution are described in detail. Components specific to the mosaic data model and its associated data dependencies are detailed as low-level design components. Subsequent to the implementation of the system, a technology assessment was undertaken to identify the raster workflow benefits associated with raster data model enhancements. Critical analysis of both the strengths and weaknesses of the system with respect to current and future business operations is provided. In summary, the original objectives are revisited to assess their achievement and future considerations for the continuing management of the system are investigated.

California is known as the "golden state" and "the best coast" to the modern generation. Therefore, it is not surprising that in 2014, the U.S. Census Bureau reported that California was the most populous state in the country. California is also filled with tremendous diversity that blends and molds various demographic groups, cultures and lifestyles, which makes the state more vibrant and appealing. Known for its warm climate and being "always sunny" seems to outweigh the negative environmental factors (i.e. landslides, earthquakes, tsunami, air quality problems, and water shortages) that may persuade people not to move to California. This diversity varies across counties and cities within the state. Aside from history and political factors, this diversity has also created significant variation in wealth and the standard of living across the region. Each county and city independently strives to increase its economic wealth and standard of living. This can be seen more dramatically in California compared to other states. There are many factors that measure and indicate the level of overall prosperity. Understanding this can decipher why certain types of people are living in or are inclined to reside in specific locations. This study used two products - the Tapestry Segmentation Product produced by Esri and the American Community Survey produced by the U.S. Census Bureau - to look at demographic and socio-economic attributes in three neighborhoods: Venice Beach, the City of Manhattan Beach, and the City of Santa Monica. Spatially visualizing these neighborhoods will easily and effectively identify the lifestyle attributes that draw current and potential residents, and show how sensitive these outcomes are to the choice of source data aggregation level and the geographic granularity that is thereby embedded in these data products. The results show how the aggregation of median household, median age and population at the ZIP code scale hides considerable variation across the three study communities. This outcome - the realization that xi the characterization of areal unit changes with the choice of aggregation level - demonstrates the importance of the modifiable area unit problem and the need for care in matching the resolution of the geographic data used to the problem or opportunity at hand.

Research studies show that urban green spaces promote physical activity, health of urban residents, and psychological well-being. However, most public urban green space is not distributed equally and fairly. In addition, access to public green space is often stratified based on race and income level. The objective for this research is to assess the level of environmental justice in the city of Phoenix, Arizona, and to answer the following questions: 1) how accessible are public parks or green spaces within a walking distance of 0.5 miles for White, Black, Asian, Hispanic, and American Indian populations; and 2) which areas need more public green spaces or parks? The accessibility of public green space refers to the distance travelled from a residential area to the nearest public green space. This study utilizes network analysis to investigate how accessible public parks or green spaces are to residents of the City of Phoenix, categorize by race, and which areas need more public green space in the City of Phoenix. A geodatabase from the US Census Bureau with pre-defined shapefiles and demographic data, as well as city parcel shapefiles from the City of Phoenix Open Data Portal are combined using the intersect tool in ArcMap. Results show that the White population does not have a higher percentage that live nearby public green space. The Asian population has the lowest public green space accessibility and the Hispanic population has the highest public green space accessibility, but also the highest park pressure. According to the future possible green space locations analysis and park pressure analysis, the demand of public green spaces for Whites and Hispanic people are the highest as compared to other groups. Given these research findings, this study suggests that geospatial analysis should be utilized in future environmental justice scholarship.

Walking and biking have always been important ways for children to get to school, but these modes of transportation have declined significantly in recent years as the majority of children now arrive to school via bus or automobile. Importantly, walking or riding a bike to school can help students; not only does it promote better health, but it also improves student concentration and academic success. Studies have shown that students who walk or ride their bike to school perform better due to the exercise they receive prior to beginning their learning for the day. For this reason, this thesis focuses on finding suitable locations for schools that promote pedestrian accessibility and student walking. Greenville County, in South Carolina, is used as the study area for this thesis. The question that this site suitability analysis (SSA) examines is the following: where are the best possible locations for new schools that meets the district's needs and maximizes the student's pedestrian accessibility? This study uses seven different criteria to determine the most suitable location for an elementary, middle, and high school to answer that question. The data are analyzed and a few suitable locations are identified. Then, the data are scored using population density and pedestrian accessibility. Lastly, the results are tabulated to reveal the best possible location for this SSA. This study could serve as a guide for future planning committees, school boards, districts, or city developers to help determine how and where schools should be placed throughout the country

The term "cultural geo-history" describes the specific connection of the culture in a given area to their environment and geographic space. For this project, the Peoples of Washington (POW) historical archive was cataloged based on GIS techniques, geocoding protocols, and the Describing Archives: A Content Standard (DACS) to create an intuitive and familiar tool for historical researchers and archivists to better understand the cultural geo-history of Washington State. The resulting tool, the Peoples of Washington Historical Geographic Information System (POWHGIS), combines a geodatabase and a web application to provide access to a small portion of the cultural history of Washington State as well as supplemental data from the Washington State Geospatial Portal and the U.S. Census. The application was beta-tested by users in order to evaluate the functionality of the tools, to provide adequate validation of the POWHGIS project and procedures. The POWHGIS project demonstrates that archival standards are useful in creating an accurate, informative, and usable HGIS tool that can increase the knowledge of and access to Washington State's cultural geo-history.

As Global Climate Change (GCC) becomes an increasingly pressing issue, the shift towards renewable energy has renewed urgency. Not only will renewable energy bring us closer to energy independence and energy security, but it will also yield cleaner air and mitigate the severity of the effects of GCC. Solar currently generates roughly 2.7% of the United States' power, leaving much room for improvement. Small-scale photovoltaic farms tend to be cheaper and easier to develop than their larger counterparts. As decentralized energy plays an important role in energy security having many small plants rather than few large plants prompted the inspiration for this thesis. This thesis incorporates GIS techniques to determine suitable sites for small-scale (1-20 MW) fixed-axis flat panel photovoltaic (PV) solar farms in Fresno County, CA. By employing street, parcel, and zoning data from the Fresno County website, Digital Elevation Model (DEM) data from the United States Geological Survey (USGS), and transmission line data within Esri's ArcGIS 10.2 (and tools such as the Area Solar Radiation and Slope tools), this thesis identifies suitable sites for a small scale fixed axis photovoltaic plant. By visualizing this information, we provide a guide for developers to reference for future developers, government decision makers, and researchers. Ultimately, this thesis was able to take a large sample size of available land, and narrow it down to a few thousand parcels based on suitability in the categories listed above, and illustrate it on a map in a meaningful way. A key observation that resulted from this study was that a lack of human infrastructure and terrain were the primary limiting factor when it comes to site suitability for PV plants, which is also a common observation with many previous studies.

Climate change is a pressing issue, and regional studies play an important part in understanding the impact of global climate change. This project explored the spatial and temporal patterns apparent in temperature records from 1935 to 2014 using homogenized station data from 66 stations in Southern California. Using Hurst Exponent, an index used to explore the persistence of trends in longitudinal data, the strength of the increasing temperature trend observed at every station was evaluated. Hurst Exponent values were calculated for the high, mean, and low temperature series for both the summer and winter 3-month period. The spatial distribution of each of the six Hurst values was examined with respect to location, elevation, aspect, land use, and population density of each station using Microsoft Excel and ArcGIS. Results show that there is persistence in the increase of temperature at all stations beginning around 1980, though the strength of this persistence varies. Winter High temperature persistence is strongest in coastal areas and weaker in the inland mountains as shown by the hot spot analysis.

A high-water mark (HWM) is a horizontal mark left on a structure or vegetation after floodwaters recede. HWMs provide engineers and floodplain managers insight into flood events because they represent the highest elevation of flooding at peak river stage. Cataloging HWMs after a flood event and referencing them to a corresponding peak river stage, allows an engineer to evaluate the impacts caused by the corresponding river stage. The river stage can be determined by utilizing the national network of streamgages maintained by the United States Geological Survey (USGS). Collecting and cataloging data from a HWM and the corresponding streamgage is valuable because the data provides a reference for engineers to calibrate and validate hydraulic models, and the data provides a reference of the impact elevation for when a future flood event is forecasted to exceed or reach the same river stage. Currently, collecting and cataloging HWM data involves a manual method where emergency management personnel and engineers fill out paper forms, and then a professional land survey crew surveys the HWM to determine the elevation of the mark. Furthermore, the attribute data collected on the HWM is not standardized, meaning that different federal agencies collect different attributes. This thesis presents a standardized method for cataloging and collecting HWM data using a mobile Geographic Information System (GIS) application for HWM data collection and a standardized digital repository for HWM cataloging and sharing. Both the application and the repository developed in this thesis provide a standardized and automated approach to HWM data collection and dissemination including direct download. Also, this thesis provides a method for the user to reference the HWM to a corresponding river stage by offering the ability to query the USGS streamgage network to find the nearest streamgage to the HWM during the field activities. The application was field tested by hydraulic xiii engineers and flood operation managers as part of this thesis work, followed by an online survey conducted to collect feedback from the users. The results from the field tests and online user survey will be used for future refinement of the applications, which has been offered as an enhancement to existing HWM data collection, storage, and dissemination strategies currently in use by the US Army Corps of Engineers (USACE) and the USGS.

Geographic Information Systems (GIS) have been widely used for archaeological predictive modeling since the 1960s. For coastal archaeology, predictive modeling, which is the practice of using mathematical models to indicate the likelihood of archaeological site locations, cultural resources, or settlement patterns, is especially helpful in locating sites potentially endangered by coastline erosion and destructive forces. The purpose of this project was to determine if it is possible to predict the presence of unknown archaeological sites along Virginia's Chesapeake coast to aid in their preservation and site management. In order to predict the presence of sites, a baseline of favorable environmental conditions was determined from known coastline archaeological sites. Environmental variables considered include elevation, slope, wetland type, land type, and distance to the Chesapeake Bay. In order to explore if these environmental variables can be used to determine locations favorable to the establishment of campsites, spatial data about these environmental variables were used in two predictive modeling methods: fuzzy overlay analysis and maximum entropy. Each model's outcomes were compared with known site locations in order to determine their success. The results of each model successfully indicated areas of site location suitability. Although results for each model varied, the trends produced were similar. Finally, in order to better prioritize site management, a risk analysis was also conducted of perceived threats compared to areas in which the models predicted site presence. These risk areas were calculated using data on human degradation and coastal sea-rise threat. As this study demonstrates, using models to predict where potential sites can allow archaeologists to prioritize areas to study for resource management purposes.

Restoration and management of ecologically important sites depend on an understanding of reference conditions and the ability of people to return the site to those historic conditions. Historical ecology research sifts through the data about a site to be able to offer restoration options to land managers. This project demonstrates transitions in natural communities of a protected area in East Central Florida: Split Oak Forest. Natural communities are defined based on the General Land Office (GLO) survey maps and notes and applied to historical black and white aerial photos, modern digital orthophotos, and high resolution satellite imagery. Because of the channelization of the Kissimmee River and the subsequent draining of the Everglades from 1883 onward, Split Oak, like other areas whose surroundings have been drained, cannot be returned to the conditions at the time of the GLO survey. Thus, a detailed time series of eight snapshots over 171 years will be valuable to land managers and restoration ecologists working in sites that share the hydrologically-modified Northern Everglades watershed with Split Oak. Natural community descriptions gleaned from the surveyors maps and notes and their application to current land cover are a potential backbone to future historical ecology in the southeast. Seasonally re-hydrating drained wetlands is a priority in this watershed, and is supported by cost-share funding from the State of Florida. This research affirms that most grassy wetlands on the site have transitioned to upland communities. Most of the remaining marshes have been invaded by woody plants and swamps extended their boundaries. Sandhill was used for orange (Citrus x sinensis) culture and, along with scrub and flat pine, transitioned to hammock.

This study provided an empirical comparison of static and animated cartographic representations of spatiotemporal phenomena in their application to basic choropleth map-based knowledgeextraction tasks to answer the following research questions: 1) Do animated maps provide heightened potential for accuracy in completing basic knowledge-extraction tasks over static time-series maps, or vice versa? 2) Do animated maps provide heightened potential for efficiency in completing basic knowledge-extraction tasks over static time-series maps, or vice versa? and 3) How do user preferences align or not align with measurements of accuracy and efficiency? To this end, this study examined map readers' accuracy and efficiency in completing knowledge-extraction tasks through static and animated time-series maps about homicide patterns in the Chicago metropolitan area. Through an online user performance experiment, participants answered a series of questions about homicide hot spots and cold spots using both static and animated versions of the maps as the basis for their answers. They were also asked to indicate their level of confidence in the accuracy of their responses and to indicate which map type they preferred for completing the tasks. Task completion times were recorded for efficiency measurements. The results of independent samples t-tests indicate statistically significant differences between the static and animated maps in terms of task accuracy and completion time. Generally, users were able to complete the assigned tasks more accurately and much more efficiently using the static maps, as compared with their animated counterparts. Additionally, user-preferences were checked for correlations with task accuracy and completion time via Pearson's product-moment correlation coefficient calculations. The results indicate no significant correlations between performance measurements and user-preferences.

The importance of social media-borne influence has been demonstrated in dramatic fashion on a global stage, with examples ranging from the regime toppling Arab Spring between 2010 and 2012, to the startling ascendency of ISIL in 2014. The value of this influence however, is highly versatile in application, and not limited to geopolitics. Commercial marketing campaigns hinge on the propagation of their message through social networks, and social media influence practitioners have engineered methods of ensuring optimal results. This practice however, is often conducted solely in a virtual environment, where false positives can abound due to disconnection from geospatial ground truths. I have outlined a system to reduce network uncertainty and identify key influencers in a manner that improves upon existing analytic processes by geospatially decomposing nebulous social media networks into locally relevant networks, wherein tangible results are more likely. This study introduces a novel approach, demonstrating that position in a social network has bearing on an individual's relationship with others in physical space, and as a result, individuals or organizations postured to influence a network via direct conduits such as local leadership figures and on-site organizers, possess a qualitative advantage. Additionally, because there exists a reciprocal relationship between an individual's position in a social network and their position among others in physical space, geospatial assessment techniques can be used to infer social connections. Dubbed endemic socio-spatial latent variable modeling (ESSLVM), this method has been automated as a Python tool that can be integrated into ArcGIS. Concepts are demonstrated using a Twitter dataset from the lateNovember 2014 protests in Ferguson, Missouri.

Wildfires are a complex natural hazard that are destructive, yet essential to many ecosystems in the western U.S. Influenced by a variety of factors, they cause inevitable social losses and damage to economic resources despite the effort of humans to predict their occurrence and spread. While past research has improved the application of various fire management strategies and fuel treatment techniques, it has also indicated a trend of a growing wildfire season with more frequent large and intense fires. The state of California, with high amounts of burnable vegetation and plagued by drought, has experienced these changes first-hand. In early February of 2015, the Round Fire burned over 6,500 acres of public and private land in Inyo National Forest and neighboring Mono County of California. Primarily a brush fire, the wildfire spread quickly due to strong winds and dry conditions, destroying 38 residential structures. Occurring both outside the regular wildfire season and during a period of historic drought, the circumstances under which the Round Fire occurred make it an interesting target for analysis. The research objectives of this study were to create a remotely sensed burn severity map of the Round Fire using the differenced Normalized Burn Ratio (dNBR) and to provide descriptive and statistical summaries for the landscape variables pre-fire vegetation, pre-fire fuel, elevation, slope, aspect, and fire history. Linear correlation between landscape variables was determined using Pearson's bivariate correlation and all ordinal data was tested for significantly different distributions amongst samples between burn severity classifications using the two-sample Kolmogorov-Smirnov (K-S) test. An accuracy assessment of the dNBR was conducted using verified soil burn severity points collected post-fire by Burned Areas Emergency Response (BAER) teams. A geospatial analysis of the Round Fire will not only assist with short-term recovery efforts, but help forest managers predict and mitigate future wildfires.

An appropriately sited retail location can turn a business into a veritable cash machine for the owner. Siting a store location has financial implications for store owners, banks, real estate professionals, store employees and company shareholders, all of whom are impacted by the success or failure of a store. Determining catchment population -- the population within a store site's actual or potential trade area -- is essential for good retail site suitability analysis. An accurate calculation of a store's catchment population depends on the method of defining a store's trade area and the accuracy and the precision of population data. This study explored how concentrating aggregated census population into existing developed residential areas affects the results of trade area analyses likely to be used in retail real estate marketing and decision making. Different methods of defining trade areas were also used to explore how the differing trade area outcomes affected results of analyses used for retail real estate decision making. It also seeks to show how different store sites with different population densities ranging from very dense areas in suburban areas to areas bordering rural areas affect population aggregations. Results of these analyses showed only small changes in catchment population and demographics when concentrated population areas were used in calculations as opposed to census aggregates. Conversely using different distance measures for trade area creation resulted in large differences in catchment population which should be taken into consideration for analysis and marketing moving forward.

Mecklenburg County is growing at an alarming rate and as a result the region is faced with the threat of rapid land use change. Since 2000 the population of the region has grown by 32 percent and the United Nations estimates an additional 71 percent population increase by the year 2030, placing it amongst the fastest growing metropolitan areas in the country. This growth is driven by sociodemographic, economic, and biophysical factors such as: an expanding young professionals demographic, high quality of life, proximity to outdoor recreation, and booming manufacturing, travel, energy, sports, and financial industries. Due to these trends it is crucial to project the magnitude and location of future expansion for the region to aid and support sustainable decision making. Visualizing how land-use change will be spatially distributed, and where competing land-use classifications will be in conflict, leads researchers to examine alternative scenarios and actions for the future of a region. This study isolated and quantified land that will be in potential future conflict, and examined four future land-use scenarios for Mecklenburg County, NC using an adaptation of Margaret Carr and Paul Zwick's Land Use Conflict Identification Strategy (LUCIS) model. LUCIS is a goal driven Geographic Information Systems (GIS) model that produces a spatial representation of where agriculture, conservation, and urban land?use suitabilities will be in future conflict and helps illustrate potential future alternative land-use scenarios (Carr and Zwick 2007). The analysis' results highlighted the escalating drive for future urban expansion into agricultural land, the persistent effort to conserve only those lands currently in conservation, and the continued push of agricultural land to the county's periphery. In addition, the four future land-use scenarios provided a simulated, potential view of the future through the lens of stakeholders who represent the interests of each land-use designation. Overall, this study successfully yielded the requisite information products for utilization by actual stakeholders to iteratively work through similar modeling efforts to assist future planning efforts.

Imagery and spatial data collected from different tools and satellite technologies have been used to complete land cover change studies at the scale of cities, countries and continents. Different methodologies have been used to complete these studies, dependent upon the technology and information available to complete land cover change. In this thesis, urban land cover has been analyzed by applying Landsat satellite imagery to spatial analysis as a way to examine land cover changes in Pasadena, California and Inglewood, California from 1992 to 2011. The objective for this study has been to review spatial data collected from Landsat data in order to understand urban land cover change in each city. Spatial data collected from the National Land Cover Database (NLCD) have been pre-processed with color infrared composite creation and image classification tools to show land cover. Imagery from Landsat 4 has been used to help compare land cover change from 1992 to 2001 since classifications of the NLCD were different in both years. The resulting maps display the land cover changes over time from the effective application of imagery analysis to complete a pattern of land cover change over the time of twenty years. The study's findings demonstrate that cities in the same metropolitan center can have similar urban growth patterns even when they have geographically diverse landscapes. These findings underscore the importance of understanding urban grown patterns when planning for urban.

Adequate access to healthy food is often considered a basic human right and ensuring that all communities have equal access to healthy food options has emerged as a focus of environmental justice activists and public policy in the United States. Increased attention and interest in locating food deserts over the last decade has resulted in many attempts at identifying areas with insufficient access to healthy foods. Many researchers and agencies have developed specific measures of food access, but these measures and indicators have not been compared methodically in terms of food desert locations identified or populations affected. This study examines and compares how varying the definition of 'food desert' impacts the extent of food desert geographies using three of the most common food desert methodologies centered around proximity, variety and competition. The results illustrate that the areas of the Phoenix-Mesa Urban Area that are classified as food desert differ depending on the methodology being used. This study shows that anywhere from 6% - 80% of the 562 low income block groups in the Phoenix-Mesa Urban Area can be designated as food deserts and the population residing in these areas with poor access to healthy food is estimated to be anywhere from 25,000 to 233,000 residents. In spite of this wide range, the geographic overlap was high with all three methodologies. The findings illustrate a need for clearer definitions regarding conceptual differences when measuring food access.

Understanding address points and street ranges is critical for providing information quickly and accurately to emergency responders. This thesis investigates the process of updating address points and street ranges in a computer aided dispatch (CAD) environment to help improve response time for emergency services while developing a more reliable geocoder for CAD. In a geographic information system (GIS), addresses verify through a process called geocoding, a topic that is currently being studied and tested in many CAD environments. Geocoding is one of the most critical components in CAD because Dispatchers depend on it to accurately confirm the location and relay the information to first responders. Based on the applied work experience and lessons learned in supporting CAD, an exact match to the property, or calls-for-service locations, are critical and can potentially save lives. Using street ranges for address verification is not as accurate as address points because street ranges only provide an approximation of location, which can require additional efforts to locate the caller and increases response time. Ideally, Dispatchers require each call point be provided as an exact physical location. This investigation examines the City of Brea, California as a case study on GIS administration in the capacity of maintaining and updating GIS data for CAD use. Verifying emergency call requests is one of the most important functions, allowing Dispatchers to send appropriate aid expeditiously. Therefore, accurate and current address point and street range information are critical in the performance of CAD functions. The results of the research inform the fitness of use and accuracy of address points versus street ranges in a CAD environment for the City of Brea, California. Moreover, this research aims to promote greater data sharing and interagency cooperation among local, county,and state agencies in the United States.

Electoral districts drawn by independent commissions are seen by political reformers to be preferable to those drawn by state legislatures. The overtly partisan interests of elected officials, say the reformers, lead to oddly-shaped, and gerrymandered districts. To test this, shapes of districts in states with commissions are compared to those within the same state prior to the commission's establishment. Additionally, shapes of districts in states with commissions are compared to those in a selected group of states without commissions. This study tests hypotheses on two methods of measuring compactness, Reock and Polsby-Popper, and coterminosity, the congruence of district lines and pre-existing political boundaries. The study finds that each state with a commission shows no significant difference in mean compactness compared to its precommission form. However, in aggregate, all post-commission districts show a significant increase in mean Reock compactness compared to all pre-commission districts, and all districts in states with commissions show significantly less Polsby-Popper compactness than districts in non-commission states. The study also finds no significant difference in coterminosity between commission states and non-commission states. Though the true effect of commissions may not be discernible from averages, other redistricting criteria also need to be controlled for and evaluated over time.

Mountain Home Air Force Base (AFB) is located in Elmore County, southwestern Idaho. A regional aquifer is the primary drinking water source for the base residents. While current groundwater quality meets regulatory drinking standards, data collected from the U.S. Geological Survey (USGS) and Mountain Home AFB indicates a significant degradation in quality, particularly nitrate contamination. The purpose of this study was to implement a groundwater model to spatially delineate areas by vulnerability to groundwater contamination risk. The model provides a basis for evaluating the vulnerability to pollution of groundwater resources based on hydro-geologic parameters, which can help develop management practices to prevent additional nitrate groundwater contamination in the region. Two Geographical Information System-based groundwater vulnerability models using the DRASTIC method were created using generic, available data and site-specific data. The models were compared to each other, as well as groundwater quality data gathered from 25 wells (16 monitoring wells and 9 base production wells) throughout the study site to validate the model. While the results indicate that the site-specific model is slightly more reliable (56% prediction accuracy), compared to the generic data model (48% prediction accuracy), neither set of model predictions seem good enough to inspire confidence and it is clear that the results produced with the two model runs are not interchangeable. The greatest cause is relative to the small sampling size (n=25) of the wells. The small sample size limits the opportunities to conduct statistical analysis to validate the model outcomes. Additional studies would need to be performed using the same approach, but with larger sample sizes so that the sample size reported here (n=25) would not negatively affect the results.

The Mountain Pine Beetle (Dendroctonus ponderosae) is a unique indicator species in the face of climate change. Since the beginning of this century, it has expanded from its historic territory in the Rocky Mountains at an unprecedented rate. As climate variables continue to change, it is uncertain how the MPB will spread throughout the continental United States. Existing habitat models have studied the current MPB territory, but have not yet been expanded to look at how a changing climate might influence the habitable range for the MPB. In response to recent climate shifts, host tree species have become increasingly susceptible to MPB attack. As their historical habitat is consumed the MPB may also be expanding into new host species. This study applied Maximum Entropy modeling (Maxent) processes to look at habitat suitability for the Mountain Pine Beetle under future climate scenarios. Results for two different emissions scenarios for 2050 and 2070 both showed a change in the MPB's range across the United States. Habitable areas became more concentrated to cooler areas, typically at higher elevations. These models show that as climate change progresses, the Mountain Pine Beetle will be a dynamic variable in forest management across the country as it alters not only its distribution, but also impacted species. Maxent modeling techniques allow a look into the future under varying scenarios to effectively predict the impacts of climate change on the Mountain Pine Beetle and its presence in our forest system.

UAVs are becoming more common in our modern world. UAVs are mostly associated with war due to the coverage of their use in the recent wars in Iraq and Afghanistan, but have the ability to do much more. UAVs are helpful tools in assessing damage after a disaster, keeping rescuers safe while they help those in need. UAVs are useful tools in monitoring crops to ensure the maximum yield is realized. The use of UAVs is also being used for monitoring remote land areas that are difficult to reach by foot. Amazon recently received approval from the FAA to research the use of UAVs for delivering packages. The uses of UAVs are endless. Maintaining public parks is a time consuming task that requires a large staff and significant hours to accomplish in a timely fashion. Maintenance crews visit the parks on a regular basis to inspect the grounds and perform any necessary repairs and routine maintenance such as picking up trash, mowing lawns, and inspecting sprinklers, whether or not work needs to be performed at the park or not. City, county, state, and the federal government are responsible for maintaining these places for the public's enjoyment. The Great Recession that occurred in the United States from 2007-2009 caused a decline in tax revenues for governments, forcing cutbacks in parks and recreation departments and requiring supervisors to develop alternative methods of completing the maintenance with smaller budgets and staffs. UAV technology is a possible solution to the problem. UAVs can be flown at any time, can capture high-resolution imagery, and require little labor to operate. This paper examines the use of inexpensive UAV technology to monitor a park for maintenance purposes. A method for using the UAV for data collection is outlined and carried out at Deleo Regional Sports Park, a public park in Temescal Valley , an unincorporated area of western Riverside County in Southern California.. The results of the UAV data are used for digitization and creating Normalized Differential Vegetation Index (NDVI) output. The results of the digitization and NDVI output are compared to ground truth data collected with a GPS receiver and NDVI outputs created with United Stated Geological Survey (USGS) Landsat 8 imagery for accuracy. Lastly, the observations of the results of the study are examined to determine the cost benefit of using the UAV versus a GPS receiver and hiring manned aircraft.

This thesis project consists of the development of a contributory web application to display and gather information on French Colonial era Art Deco architecture in Hanoi, Vietnam. The goal of this application is to create the foundation for a web-based spatial inventory of existing French Colonial era buildings. The inventory is meant to advise policymakers and heritage organizations on priority resources to protect the existing base of resources as well as to create a historic record of the historic urban landscape. This is important because the push to modernize infrastructure in emerging nations often leads to the destruction of the colonial heritage fabric in urban areas. An inventory of what currently exists, and possibly what existed in the past, will help to digitally record these sites, despite what occurred or may eventually occur in the physical places. Also as part of its purpose, the application seeks to engage the general public, including interested heritage professionals and scholars, by incorporating the ability to contribute information through the correction and enhancement of current entries. The web application will incorporate the minimum data standards for inventory of cultural heritage as specified in the Core Data Index to Historic Buildings and Monuments (Thornes and Bold 1998). The initial dataset for the application is based upon data collected during fieldwork in June 2012 and covers a self-defined area of the French Quarter district of Hanoi. As a secondary purpose, the web application was developed to be replicable by others who might choose to take and adapt the web application model for their own heritage conservation-related purposes. As such, the web application employs easy-to-use, relatively inexpensive, cloud-based tools and services, such CartoDB, MapBox, Persona, Heroku, Bitbucket, and various Google products. In order to use many of these services together, the web application was purpose-built using the well-documented and flexible Python web development framework, Pyramid, in conjunction with the templating system, Jinja2, along with the standard HTML, CSS, and JavaScript programming languages. After instructional documentation is written and further development occurs as specified in the application's roadmap, the code, which is currently stored in a private repository on Bitbucket will be opened for download and collaboration by others wanting to use and improve the application model.

Progress in information and communications technology (ICT) has enabled members of the general public to contribute to data collection that has traditionally been reserved for trained professionals. Volunteered Geographic Information (VGI), user-generated content with a geographic component, is becoming more widely available with an ever increasing range of data types (Fast 2014). This study extends previous analyses of VGI by investigating a first-of-itskind dataset, known as Fatal Encounters (FE), which seeks to collect information on incidents involving police use of deadly force on citizens within the United States. Geographers recognize the potential for VGI to enrich existing forms of authoritative data or produce new data, but the consensus is that VGI can be highly variable in quality. Relevant quality components are used to build a framework for validating the FE dataset. The main components include completeness, spatial accuracy and precision, and attribute accuracy. Once these components are assessed, the overall fitness of the FE dataset is determined with an evaluation of its strengths and weaknesses. The resulting analysis showed that the dataset was sufficiently complete for initial spatial analysis, but lacked fitness for specific attributes. Based on fitness of the data, the study also conducts a preliminary hotspot analysis for these incidents in New York City, including an overlay of hot spots on population density and a race-based dot density maps. Before further analysis can be done, recommendations for improving the weak portions of the data are discussed.

Digital information with a spatial component is being generated at an astounding rate, from sources such as Flickr Videos, online news, and "tweets" on Twitter. The ability to identify locations in unstructured text and quickly generate a map unlocks valuable information about the context of the locations in the text. Geoparsing, the process of assigning geographic coordinates or other geographic identifiers to unstructured text, extracts this valuable information from text (Nikolajevs and Jekabsons 2013). Existing studies and tools focus on the challenges of location extraction and disambiguation. These studies do not focus on visualizing the extracted locations, and generally use a simple method of displaying each location as a single point on a map. This thesis examines the current geoparsing text-to-map applications, identifies challenges to generate a map from a text document, and defines an approach to display locations with boundaries and relationships between locations on a map. The outcome of this thesis is a semi-automated geoparsing, data integration, and visualization application to convert the locations in text-based news articles to locations on a map. This approach provides an efficient and effective way to display the spatial context of a text document and allow for interpretations of the data that is not readily apparent from the text by itself.

Long-term remedial action Superfund sites pose steep challenges for the Environmental Protection Agency (EPA) and stakeholders to remain actively engaged in cleanups that could go on essentially in perpetuity. It is essential for communities impacted by Superfund cleanups to actively participate in the cleanups so that they may be part of the decision-making process. Citizens directly affected by Superfund cleanups have unique perspectives, information, and spatial knowledge to contribute, but opportunities for participation in Superfund may be limited to the agendas, meeting spaces, and timelines of the EPA (Laurian 2004). In the City of Los Angeles, the Del Amo and Montrose Superfund sites are located adjacent to each other and directly north of an unincorporated neighborhood of approximately 300 households. Due to the extent of the commingled groundwater contamination originating from both sites, it is understood by the community that the time frame for cleaning up the groundwater will span 3,000 to 5,000 years. The primary goal of this thesis was to understand and portray the cleanup through the perspectives of local community members. Specifically, the objectives of this research were to: (1) use a community-engaged research approach to develop a Web GIS Story Map which incorporated experiential spatial narratives from the perspectives of local citizens affected by the Del Amo and Montrose cleanups; (2) ensure that a critical evaluation of the Story Map was possible on behalf of participants throughout the development of the tool; and (3) promote the Web GIS tool to stakeholder groups and other entities for feedback and evaluation. The Web GIS Story Map combined interactive Web maps and mixed media to communicate the history of the Del Amo and Montrose sites, as well as how the community has been impacted by the cleanups and the contamination over the past two decades. This project demonstrates how a community-engaged Web GIS Story Map can facilitate dialogue among various stakeholder groups invested in the cleanups. In addition, this study recognizes the potential for regulator stakeholders to assist in developing more robust geospatial visualizations of intended remedial objectives.

New supply chain management methods using radio frequency identification (RFID) and global positioning system (GPS) technology are quickly being adopted by companies as various inventory management benefits are being realized. For example, companies such as Nippon Yusen Kaisha (NYK) Logistics use the technology coupled with geospatial support systems for distributors to quickly find and manage freight containers. Traditional supply chain management methods require pen-to-paper reporting, searching inventory on foot, and human data entry. Some companies that prioritize supply chain management have not adopted the new technology, because they may feel that their traditional methods save the company expenses. This thesis serves as a pilot study that examines how information technology (IT) utilizing RFID and GPS technology can serve to increase workplace productivity, decrease human labor associated with inventorying, plus be used for spatial analysis by management. This pilot study represents the first attempt to couple RFID technology with Geographic Information Systems (GIS) in supply chain management efforts to analyze and locate mobile assets by exploring costs and benefits of implementation plus how the technology can be employed. This pilot study identified a candidate to implement a new inventory management method as XYZ Logistics, Inc. XYZ Logistics, Inc. is a fictitious company but represents a factual corporation. The name has been changed to provide the company with anonymity and to not disclose confidential business information. XYZ Logistics, Inc., is a nation-wide company that specializes in providing space solutions for customers including portable offices, storage containers, and customizable buildings.

Vernal pools are rare, seasonal pools that form in landscape depressions and create temporary habitat for many floral and faunal taxa. In California, as much as 90% of historic vernal pool area has been displaced by agriculture and urbanization. Pools are commonly inhabited by endemic, threatened, and endangered plants and animals, and are critical breeding areas for California tiger salamanders (Ambystoma californiense) and fairy shrimp (Linderiella occidentalis). Seasonal inundation and desiccation are driving factors behind the biotic community structure around pools, both spatially and compositionally. At Fort Ord, California, a rare subset of vernal pools occur perched atop relict sand dunes in an arid chaparral environment. Fifteen vernal pools have been previously identified within the base's historic firing range impact area. At least 45 other lowland meadows within the impact area meet pool topographic requirements and were evaluated for their potential to be vernal habitats. This thesis proposes an object-based method of extracting vegetative patterns from VHSR Ikonos and WorldView 2 satellite imagery, to compare persistence in vegetative patterns over time. Classification results from three aerials collected over an 80-year interval were subjected to a geospatial change analysis, and used to make short- and long-term comparisons of known vernal meadows to themselves and other meadows in the study area. Two new metrics, the Persistence Index and Weighted Intervals Persistence Index, were created for this study. These indices normalize changes in geometric properties, enabling comparisons between known vernal areas and study sites, and between self-same sites sampled at different times. PI and WIPI results were consistent with the results from other analyzed metrics. Strong persistence in several study sites, comparable to that of the known vernal areas, likely indicates latent presence of a seasonal hydric regime and an elevation-based hydrological gradient. The results of this study show that there is no statistically significant difference between the way that vernal and other meadows change shape and size over time. This result means that a number of lowland meadows in the impact area may have active or dormant vernal pools because the two groups cannot be empirically differentiated from one another. This study also positively confirmed the presence of at least five previously unrecognized vernal areas through the detection of water in multiple aerial images. These findings merit further on-the-ground investigation, as well as a geographical reconsideration of current conservation efforts.

A smart grid is an energy grid network upgrade to a system that captures waste heat, adds detail and visibility to household monitoring techniques, allows for compatibility with remote alternative energy sources, and transfers data from meters to communication towers, also known as Data Collector Units (DCUs) using wireless technology. California is mandated to provide 33 percent of statewide energy from renewable sources by 2020. An energy network upgrade to a smart grid would facilitate the remote storage and transfer onto the grid that are necessary for solar and wind farms, which are often located far away from dense urban centers. Past research on smart grid development has focused on maintaining optimal meter to communications tower readings through analysis of distance to meters, spacing throughout a region, elevation and slope. Some site suitability research has focused on optimizing wind turbine placement so as to reach customer regions while simultaneously not offending nearby residents and staying clear of housing viewsheds. One particular power plant site suitability study used an ArcGIS weighted overlay analysis to return a score of one to 10 as a final site suitability predictor. This project incorporated ideas from each of these analysis approaches by including a viewshed analysis of meter-to-communication tower dynamics, communications tower site acquisition variables needed for placement objectives, and a pass/fail scoring system reflecting each variable. The site suitability tool meets the meter visibility objective of quantifying line-of-sight, nearest feature association, and distance between utility meters and communications towers. Three site acquisition objectives were considered: (1) pinpointing tower locations within 20 ft of a publiclymaintained street; (2) placing towers a minimum of ten feet from power lines for safety reasons; and (3) determining location(s) that are likely to avoid tree obstructions, so that radiation is sufficient to meet the needs of communication tower solar panels.

Counts of the number of bicyclists on roads give community-based organizations strength in appealing for improved bike infrastructure from city governments. Bicyclist count data can also be used in conjunction with vehicle counts and collision data to better understand factors that contribute to motorist-bicyclist collisions. Bicyclist count collection involves manual methods where volunteers fill out paper forms for bike coalitions, and automated methods such as video cameras set up on roads to capture bicyclist movement. This thesis presents a mobile application through which users generate bicyclist counts (Volunteered Geographic Information, VGI), and a website that provides a method for users to review these bicyclist counts. Both the application and website developed in this thesis contain motorist-bicyclist collision data derived from an authoritative source (Professional Geographic Information, PGI). Counting bicyclists in high collision areas can indicate of these areas see a high or low amount of bicyclists, making the motorist-bicyclist collision PGI germane. The bicyclist count collection method produced by this thesis serves as a model for community-based organizations that want to collect bicyclist counts by means of an inexpensive and automated method.

Training soldiers for combat is necessary to mitigate casualties of civilians and soldiers in the field during wartime. An advanced system of training has been developed that prepares soldiers for war by simulating combat scenarios and tracking a soldier's location and if they are shot. The data acquired from these training scenarios has the potential to inform training doctrine and improve combat performance. The use of Geographic Information Systems (GIS) to analyze fatalities in the training exercise has not been implemented to explore ways performance might be improved. This study used data acquired at an Army National Guard Exportable Combat Training Capability (XCTC) training event at Fort Drum in New York on the 7th August 2013 to visualize the numbers of unique persons travelling through a cell during the day as well as the average number of people in a grid cell within 30 seconds of an engagement, hot spots of engagements on a linear network, and how the number of people and engagements changed across the field site at 15 minute intervals throughout the day. The output can then be used in the daily After Action Review (AAR) in conjunction with the training playbook and mission objectives to assist soldiers and commanding officers in clarifying what factors are contributing to the hot spots. The results might then be used to require training iterations under specific scenarios to improve training performance.

Archaeological investigations are an integral part of the permitting process for land development in Hawai'i. The State recognizes that conservation of its historic and cultural heritage is important and that its cultural resources are nonrenewable. A recent archaeological survey for prehistoric and historic agricultural features in the Hokuli'a luxury development on Hawai'i Island afforded an opportunity to test maximum entropy theory as a method for predicting the presence of archaeological features. The Maxent computer program uses a machine learning algorithm that utilizes presence point data and environmental variable rasters to produce a probability distribution of the species of interest. The "species" of interest for this research were agricultural clearing features, associated with sweetpotato (Ipomea batatas) cultivation, identified in a portion of Hokuli'a which lies within the Kona Field System. Previous agricultural habitat suitability models for Hawai'i were used to determine the environmental variables used in this research; the variables included annual rainfall, summer rainfall, elevation, and slope. Maxent produced a probability distribution that matched the expectations of the conceptual model. The model was validated using diagnostic tools included in the Maxent program (1) area under the receiver operator characteristic curve analysis, (2) jack-knife testing, and (3) environmental variable response curve analysis, as well as three research hypotheses. The model does not account for human behavior and may overestimate feature presence in uncultivated, spiritually important areas that are suitable for farming. The results of this research show that Maxent can be used to successfully model certain types archaeological features.

Several recent hurricanes along the eastern United States seaboard have resulted in catastrophic flooding: Hurricane Katrina (2005), Hurricane Irene (2011), and Hurricane Sandy (2012). In addition to their disastrous effect on life and property, protracted utility outages from flooding are expensive and disruptive to recovery. Utilities could be less vulnerable to flooding if company assets were protected better in advance, based on the models of predictable storms surges. The Federal Emergency Management Agency (FEMA) is tasked with hazard mitigation and response through the United States, for floods among other perils. FEMA's HAZUS [Hazards US] software included modules for predicting flood extents in response to stream discharges (inland) and coastal surges. The National Oceanic and Atmospheric Administration (NOAA) also makes predictions of storm surges via its SLOSH [Sea, Lake, and Overland Surges from Hurricanes] maps. Both HAZUS and SLOSH rely on geographic information systems (GIS) technology. This study compares the FEMA HAZUS and NOAA SLOSH model predictions against direct flood measurements for the Hurricane Sandy "Superstorm" that damaged extensive areas of New York and New Jersey beginning on October 29th, 2012. Focus is placed on differences in predicted vs. observed flood inundation for key utility asset and infrastructure locations, especially in flood hazard zones. For Superstorm Sandy, SLOSH produced more accurate flood predictions than HAZUS for New York City.

Uncontrolled populations of feral cats in urban settings have become of concern to public officials, wildlife scientists, animal rights advocates and the public in general due to the risks they pose to public health, urban wildlife, and esthetics. Solutions to the problem of unmanaged cat populations in cities have been limited in scope by the lack of actual data on feral cats and the urban geographic ranges they occupy. Full extent censuses and environmental analyses have not been collected or performed due to the resources allocations and costs involved. A method for collecting this data without the use of field crews and research summaries exists in the form of unused paper records. Past studies on the problem have used data mining of available records to model cat territories and densities (Aguilar and Farnworth 2012). This approach mitigates the cost while providing information regarding the distributions of these animals. This thesis investigates the spatial properties of feral cat populations in a large metropolitan area (Los Angeles, California) using a previously non-spatialized dataset as a proxy for concentrations of feral cats. The following case study explores two matters: 1) development of a workflow to create a spatial model of feral cat extents from geographic data brought into an analyzable format and 2) analysis of the model data to determine what, if any, variables are correlated with these distributions. The data used for the model were obtained from the City in the form of paper records and successfully imported into a Geographic Information System. Densities of applications were determined from the cleaned and geocoded records and concentrations of both raw density and patterns of clustering were mapped. Modeling of correlations found positive associations with population density and a weak negative correlation with median income. The analysis was assessed and future work on this type of data was considered.

The goal of this thesis was to develop a process in which historical land use can be tracked in order to gain a better understanding of an area's history. The study area, Long Island City (LIC) is historically an industrial neighborhood within Queens County of New York City. By documenting its land use shifts from 1891 to 1950, it is possible to visualize and analyze the changes that occurred as industrialization took place. This study compiles a digital historical narrative to provide a foundation for understanding the data, as well as a reference for making new conclusions from the results of the analysis. Old fire insurance maps provide building footprints categorized by use. These were used to digitize locations of interest as points that were catalogued under five different categories: Cultural, Industrial, Residential, Shop, and Vacant at each of five time periods. The resulting spatiotemporal database makes it possible to track a single building and its use through a period of 59 years. The methodology developed for this thesis collects and classifies building use as points so as to develop efficiently and quickly an accurate historical dataset. In doing so, the project tracked the cultural development of LIC through an examination of a set of key buildings, as well as the overall land use change of a sub-neighborhood, Hunter's Point. It determined that by tracking the use of every building through every map year, one gets a better historical analysis. Such methods can be used not only to help support previously known historical narratives, but also to allow for new conclusions to be drawn.

In the world of professional sports, stadium construction is a venture that can cost communities hundreds of millions?sometimes billions?of dollars. While the process of selecting a site based on human or political motivators (i.e.: Quid pro quo, public subsidies, etc.) is dubious at best, the process of selecting a new site based solely on geographic factors (such as ease of accessibility) is even more ambiguous. Historically, new sites were located within a city's limit and closer to population centers, but within the mid to late 20th Century, this paradigm was abandoned and new stadiums were placed farther from the cities that the teams represent. To identify a new location for the Tamp Bay Rays within the Tampa Bay area, this study used socioeconomic (population concentrations), traffic (accessibility), and geographic (parcel and land use) data to determine where throughout the region will be the most viable location for a new stadium facility. This research analyzed the population and the geographic construction of the region and identified variables and parameters that determined the locations that could best support the team throughout the region. The findings of this study show that, by applying site suitability methods, the team can be sustainable within the Tampa Bay area and that by selecting a site closer to the population center of the region, success off the field can be achieved.

Almost every country in the world has experienced a border dispute to varying degrees of conflict and the Sudan - South Sudan border region is no exception. Distribution of spatial information to all sides in border negotiations may help to ensure a smoother functioning negotiation, and thus avoid armed conflict. In this thesis, the likelihood of border conflict is measured by adapting the opportunity and willingness framework, and then determining the conflict border (Starr 2002; Starr and Thomas 2005). Conflict occurs where the border region has infrastructure in place to mobilize militarily and the area is salient, but not so salient that mutual cooperation between states has occurred. This thesis demonstrates the utility of a GIS analysis for border placement negotiations between Sudan and South Sudan by developing a conflict border index based on the opportunity and willingness distribution within a 100 kilometer border region. The opportunity and willingness analysis proves effective in determining the utility of GIS in border determination. Areas with medium levels of opportunity and willingness were located and therefore could be avoided in border placement as a way to reduce potential future conflict.

Disparities in food access to different types of food stores are a key factor in assessing the health of food environments. The spatial accessibility of food (hereinafter "food access") refers to the physical distance between food stores and the neighborhoods they service (Sharkey and Horel 2008; Larson et al. 2009). Nationwide studies of metropolitan and urban areas have shown that low socioeconomic areas have fewer supermarkets and more convenience stores than high socioeconomic areas (Morris et al. 1990; Cotterill and Franklin 1995). However, some more recent studies of localized areas have found no evidence of a relationship between food access and socioeconomic conditions (Alviola et al. 2013). Still others have found that deprived minority neighborhoods exhibit better food access than wealthier areas (Sharkey and Horel 2008). Gaps exist in the literature for food access analyses at the local scale. The Atlanta-Sandy Springs-Roswell, GA MSA is one such region lacking an empirical analysis of food access at the neighborhood scale. To investigate the relationship between food access and neighborhood characteristics, this study measures road network distance of neighborhoods, defined as the population weighted centroid of Census Block Groups, to different types of food stores (chain supermarkets, small grocery stores, convenient stores, and fast food restaurants) throughout the 2010 Atlanta MSA. The primary conclusion of this study is that food access to all food store types in the Atlanta MSA is highest among high minority and low income neighborhoods. This may speak more broadly to the differences in food access between urban and rural areas as the majority of all types of food businesses are located in the densely populated areas surrounding the city center of Atlanta. Future research should investigate how urban, rural, and suburban neighborhood types shape food access in the Atlanta MSA.

This study tests the effects of the built environment on vehicle miles traveled (VMTs) and automobile ownership, with specific reference to aspects of neighborhood walkability studies and research design at nested spatial scales of metropolitan regions and neighborhoods. This adds to existing smart growth studies as they tend to focus on Census data and non-walkability land use variables such as rail transit infrastructure. This study looks at 75 census block group samples within 5 metropolitan statistical areas (MSAs). The variables measured for the study include, bus stops per square mile, jobs within 45 minute transit ride, gross activity density, temperature, distance to retail, distance to transit, and slope among others. This study also looks at including regionally measured variables such as people per transit station, MSA density, and transit expenditure in conjunction with neighborhood scaled variables in order to test if there are any interactions between neighborhood and regional variables. The variables are entered into a multivariate regression model to find the best-fit model in order to explain the relationships between the dependent and independent variables. The study finds that the new walkability variables added to the research add significantly to the explanatory value of regression models beyond studies that use just smart growth land use variables. The implications for this study are that there is ground work laid for a new type of smart growth and walkability joint study at a multiple region level.

Park planners make long-term land acquisition and capital improvement plans based in part on population growth and gap analysis of existing facilities. This study demonstrates a new cadastral-based technique to measure park access for residents in Wake County, NC. Based on road network and cadastral data, the technique uses the Origin-to-Destination Matrix Tool within Esri's Network Analyst extension in conjunction with dasymetric mapping of US Census Data to the cadastral data. The demonstrated workflow provides for a highly detailed assessment of walking distance between parcels and parks, that when linked with the population data, provides a gap analysis based on the amount of parkland and number of parks available at each parcel. Successful completion of an analysis at this level of detail illustrates a very different view of park coverage for Wake County, NC compared to traditional methods, revealing how hard edges created by major thoroughfares and soft edges created by property ownership impact pedestrian accessibility. Using the cadastral-based method, 19.85% fewer parcels have 1/4-mile park access than compared to a buffer based method (6.72% versus 26.27%). The use of this type of technique will allow for a more comprehensive assessment of the peoples served by the park system and when coupled with demographic information, may prove more effective in assessing grants and monitoring the impact of public initiatives promoting equality and uniformity of access to public parks.

The occurrence of asphaltic fossil localities within and surrounding the Page Museum at the La Brea Tar Pits in Los Angeles, California is extensive and has been recorded for decades as nonspatial data collected in a non-spatial database. The motivation for this project stemmed from the author's time as a volunteer at the Page Museum over the course of one year. The Page museum staff requested an efficient way to cartographically display fossil data to assist staff with visualizing the taphonomy of fossils. At the time of this study, this thesis is the first GIS project that the Page Museum had ever supported for mapping of fossils. Most current literature describing fossil-related web GIS applications reports data displayed at small-scales, and exact locations of fossils are not generally provided through the applications. The main objectives of this thesis project were to design and implement a fossil excavation spatial database, digitally curate data that previously only existed in paper form, display fossil data in an interactive web GIS application, and develop a framework to support spatial analysis and live data feeds of fossil data in the future. As part of this thesis project, known fossil localities were digitized from a La Brea Tar Pits survey map maintained since 1913. The fossil specimen location records from the museum's existing database were then joined to those newly digitized features to support the development of the spatial database of existing fossil localities within the park. The fossil features contained in the spatial database were then published to the web through the web GIS application also developed as part of thesis research, as a proof of concept intended to guide future Page Museum GIS projects. Visualizing the location of fossils is intended to help better communicate the paleontology of the La Brea Tar Pits to the museum staff, and eventually to the general public. Lastly, it is anticipated that this web GIS application will contribute to the current literature on documentation and visualization of extensive fossil deposits.

The Lake Tahoe Basin, California/Nevada is the setting for evaluating a species richness modeling technique that is both accessible and provides an apparently unique approach to studying forest diversity patterns. Species richness, the total number of species of a focal group present in an ecological community without regard to individual taxa, is an important indicator of biodiversity. Despite its importance to researchers and natural resource managers, predicting species richness patterns in forested landscapes is difficult and therefore, not common. The computationally powerful yet highly accessible Maxent package, specifically designed for modeling species distributions, is used to predict homogenous patches of species richness by treating species richness values as individual "species." Areas where ranges of homogenous species richness overlap are then isolated and displayed as "border regions" similar to ecotones. Nowhere in the ecological literature is Maxent used in this manner, nor are transitional zones between regions of species richness viewed as spatial entities. Therefore, this thesis investigates if Maxent can make valid predictions about species richness and if areas where species richness predictions overlap constitute transition zones. To validate the model, traditional species distribution models for each included tree species were created using Maxent, stacked and then summed to produce a comparable species richness surface. Similar patterns between the two models indicate that Maxent accurately predicts species richness from environmental factors. Border regions were validated as legitimate spatial entities using split moving window dissimilarity analysis?a technique used to identify ecotones. Results indicate that using Maxent for this application is very likely valid and species richness border regions represent a promising spatial entity for studying diversity patterns. This spatially explicit approach provides an accessible method for studying species richness patterns at multiple scales. Further, a temporal series of these models provides a method for examining how diversity changes over time.

The population of Sonoran pronghorn (SPH; Antilocapra americana sonoriensis), an endangered subspecies within the United States (US), has fluctuated from an estimated 282 individuals in 1994 to 21 in 2002 and back up to over 150 as of August 2014. As the population continues to recover from drought-associated stressors, more SPH frequent the Barry M. Goldwater military tactical range and the United States Air Force (USAF) closes more targets from training for longer periods of time. In this thesis, hotspot analyses are combined with maximum entropy distribution modeling to understand the geographic and seasonal variation in SPH distribution at North and South Tactical Ranges (NTAC, STAC) in the Barry M. Goldwater Range East, Arizona using data from a monitoring effort begun in 1997. Results show hotspots of high densities of SPH near strafing and bombing targets, supporting previous studies using fewer data. In Maxent-derived habitat models, distance from targets had the strongest effect on model performance, followed by slope of the ground. According to the models, distance from roads had no effect on the SPH locations, nor did distance from observer. Prior studies attribute SPH preference for areas near targets to attractiveness of forb growth following disturbance as forage, and high visibility resulting from few tall shrubs or bushes. Output from the distribution model provides a predictive map of habitat use that can be used to evaluate effects of range use on SPH in the future.

Climate change is a global occurrence and is studied at multiple scales within Los Angeles County, California. Determining the type of surface temperature trend across Los Angeles County is best observed using historical daily, monthly, and yearly temperature data. Each type of historical temperature data is analyzed for various temperature and extreme temperature threshold trends: (1) thresholds of frost days (minimum temperature ≤ 32°F), misery days (maximum temperature ≥ 90°F), and heat wave events are examined at six weather stations; (2) type of linear trend is measured for monthly surface temperature at eight weather stations; and (3) type of linear trend is analyzed for yearly surface temperature and yearly summer surface temperature (July to September) for twenty weather stations from 1931 to 1950 and six weather stations from 1951 to 2010.
This study’s major findings are (1) daily maximum and minimum surface temperature show strong departures from normal conditions for threshold temperature trends as Palmdale experiences an accelerated warming trend and Sandberg experiences an accelerated cooling trend; (2) a variance in decadal heat wave thresholds exists at each weather station for 80 years; (3) monthly mean surface temperature is a good source to reflect seasonal temperature variations; and (4) yearly surface temperature is not sufficient temperature data to track temperature trends. Analyzing surface temperature trends is a tool for monitoring how climate change is impacting temperatures globally.
The following chapters include: (1) introduction is the motivation and research questions; (2) literature review is previous studies on climate change and its impact on temperature; (3) data and methods are data sources and the implementation of these sources; (4) results offer a detailed explanation and examples of the findings; (5) discussion is an overview of the important findings; and (6) references are sources that are cited within the manuscript.

Field research is a necessary component of many realms of ecological and geoscientific practice since it provides the primary data crucial to understand the characteristics of an object, phenomenon, or process. Unlike work in an office or laboratory, fieldwork has additional cost related to travel, lodging, and per diem expenses. Field scientists must therefore ensure they make efficient and effective field navigational decisions that result in expedient execution of field campaign objectives. Technologies and analytical approaches such as decision analysis, path modeling, and geographic information systems offer assistance to navigational decision making while in the field as do the analytical techniques of weighted linear combination and analytical hierarchy process. These tools are often underutilized, however. This thesis describes a methodology by which these technologies and analytical procedures may assist field scientists with navigational decision making. Specifically, the thesis documents development of a model that uses a spatial multicriteria decision evaluation to derive favorability values. These values are then used to determine the placement of traverse paths that are suggested routes to be taken by field researchers. The thesis includes a description of concepts behind the methodology, a demonstration of the methodology for a hypothetical geologic campaign, and an analysis of resulting traverse paths.

Geographic Information Systems (GIS) mapping applications have proven to be an integral part in school site planning. However, most school site planning does not take walkability into account. This study describes a method to measure how walking access to schools was affected by the closure of elementary schools in the San Juan County. Recent studies in students walking to school in the US have found that there has been a major decrease overall (National Household Travel Society 2013). Using population density and dasymetric mapping, the number of students in each parcel in the San Juan School County was estimated. A walkability service area was derived from a network dataset using the functional classification of roadways from the Federal Highway Administration (FHWA). Accessibility was calculated 1⁄4 miles away from the school using the service areas before and after school closure. It was determined that after school closure pedestrian accessibility and total distance walked to school did not change significantly. Network analysis represents a direct approach that assesses accessibility and physical barriers of the urban environment. Combining the walkability service area and the population per parcel, student accessibility was calculated. The use of this methodology will allow a better assessment for the school site planning and can be used to develop initiatives that will promote walking to and from school.

High quality topographic (land elevation) and bathymetric (water depth) data is targeted by the USGS and other Federal agencies as a need for update and modernization, particularly with the rapidly advancing technological innovations for use in modeling hydrological and environmental changes. Esri's ArcGIS provides advanced and various options to interpolate surfaces using two ArcGIS Extensions: Spatial Analyst and Geostatistical Analyst. These extensions provide access to advanced mathematical algorithms used in the interpolation of measured points into an elevation surface, through a user-friendly interface with pre-defined, yet highly technical input parameters. Using Light detection and Ranging (LIDAR) elevation measurements and Single Beam Sonar on the Klamath River Estuary, this project compares interpolation methods provided by ArcGIS in the Spatial Analyst and Geostatistical Analyst Extensions, in order to determine how varying the parameter settings affect the resulting surfaces. This case employs seven commonly use interpolation algorithms: Inverse Distance Weighting, Natural Neighbor, Spline Regular, Spline Tension, Kriging, Empirical Bayesian Kriging, and Topo to Raster, all of which can be used in Digital Elevation Model (DEM) surface creation. Understanding the differences between the two extensions and modifying parameters in each interpolation algorithm results in statistically reliable elevation surfaces. The results prove that modifying the default interpolation parameters to fit the statistical variability, which is completed by the optimization of the Geostatistical Analyst Wizard, improves the functional use of the study area raster surface.

Customer feedback is a platform to share awareness about a business or service between consumers and is an excellent resource for gathering information needed to determine if that businesses or service is satisfying customer requirements. Yet, consumers will often fail to leave reviews if the process to do so is too lengthy, overly complicated, or if too much time has passed after visiting a business. Reading numerous reviews often requires consumers to dedicate a considerable amount time to compose or examine and frequently provide extraneous amounts of information irrelevant to the business reviewed. Problems also arise from the use of oversimplified rating scales that lack context and become meaningless when consumers do not know what attributes scores are based on. The existence of these issues creates the demand for a tool that can collect, compile, and deliver relevant business reviews back to consumers quickly and in a user-friendly format. The tool developed for this study consisted of a mobile application that uses the CrossFit business model and associated CrossFit affiliate locations as a case study. By using a geotrigger service, the application prompts users to provide Volunteered Geographic Information (VGI) that consists of customer feedback, in the form of a brief survey, immediately after visiting an affiliate location. The application compiles gathered survey scores and then provides them back to users in near real-time. Evaluation of the mobile application found that it assisted consumers in making more informed decisions when attempting to select which CrossFit affiliate to patronize and accomplished its intended purpose of collecting and disseminating of information obtained in near real-time directly from customer feedback.

In the years following the 1906 earthquake and fires, the streets of San Francisco were renamed, renumbered, and reshaped. These changes make it challenging to locate addresses found in historical directories, newspapers, and archives. Fire insurance maps produced by the Sanborn Map Company represent some of the most detailed sources of spatial information about early twentieth century San Francisco, but they are cumbersome to navigate. Insurance maps contain detailed street indexes that mirror address geocoders in content and function?listing street names and address ranges. Exploiting their structure, the text of these street indexes was transcribed in order to create a geocoder that identifies map sheets. The Sanborn indexes served as reference data for an ArcMap address locator. The geocoder makes the insurance maps more navigable and provides historical context for addresses.

The greater sage-grouse is a very important species in the sagebrush landscape of the western U.S. The number of sage-grouse has declined due to habitat loss. This study charts the distribution of the greater sage-grouse in the Powder River Basin in northeastern Wyoming using the maximum entropy model MAXENT. The MAXENT model used variables important to the greater sage-grouse to create rasters that emphasized suitable habitat in Campbell and Converse counties. The first model used two biophysical factors (to mimic landscape suitability in the absence of people) and the second model used seven additional layers of distance to primary and secondary roads, gas processing facilities, power lines, pipelines, coal mines, and wells. The overarching goal was to document the impact humans have on the greater sage-grouse's habitat. Greater sage-grouse data has been collected since 1948 and these observations were used to develop the final models. The performance or accuracy of the model was based on the Receiving Operating Curve (ROC) and the Area Under the Curve (AUC) using 15 replicates of both models. Both of the models were able to predict the species distribution and achieved a rating of average in terms of performance. The two suitability maps produced by MAXENT highlight where the most acceptable habitats are located within the Powder River Basin. This is based on the environmental layers that were entered into MAXENT. The output can give researchers ideas of where best to place their conservation efforts for the greater sage-grouse. The greater sage-grouse is an important species because it is only found in North America and a small part of Canada. It is considered an umbrella species, meaning other species depend on its survival. The conservation of this species will benefit many other species that consider the 'sagebrush sea' their home.

The greater sage-grouse is a very important species in the sagebrush landscape of the western U.S. The number of sage-grouse has declined due to habitat loss. This study charts the distribution of the greater sage-grouse in the Powder River Basin in northeastern Wyoming using the maximum entropy model MAXENT. The MAXENT model used variables important to the greater sage-grouse to create rasters that emphasized suitable habitat in Campbell and Converse counties. The first model used two biophysical factors (to mimic landscape suitability in the absence of people) and the second model used seven additional layers of distance to primary and secondary roads, gas processing facilities, power lines, pipelines, coal mines, and wells. The overarching goal was to document the impact humans have on the greater sage-grouse's habitat. Greater sage-grouse data has been collected since 1948 and these observations were used to develop the final models. The performance or accuracy of the model was based on the Receiving Operating Curve (ROC) and the Area Under the Curve (AUC) using 15 replicates of both models. Both of the models were able to predict the species distribution and achieved a rating of average in terms of performance. The two suitability maps produced by MAXENT highlight where the most acceptable habitats are located within the Powder River Basin. This is based on the environmental layers that were entered into MAXENT. The output can give researchers ideas of where best to place their conservation efforts for the greater sage-grouse. The greater sage-grouse is an important species because it is only found in North America and a small part of Canada. It is considered an umbrella species, meaning other species depend on its survival. The conservation of this species will benefit many other species that consider the 'sagebrush sea' their home.

Since civilization emerged in Mesopotamia, sociological conflict and sometimes-nefarious behavior, hereafter referred to as anthropogenic threats, evolved along with the societies. In modern times, the presence of people with damaging and dangerous intentions in our societies is indisputable. Societal risk induced by anthropogenic threats is seen as a growing problem by public safety and intelligence agencies as well as owners and operators of designated critical infrastructure, e.g. bridges, ports, utilities, etc. Use of geographic information systems (GIS) has become commonplace across a range of disciplines, including natural hazards and associated risk models but is not immediately useable for modeling anthropogenic threat phenomena due to their non-recurring and inconsistent nature. GIS, however, can be a powerful tool for communicating critical aspects of anthropogenic threats, particularly if a suitable symbology is available. This research applies well-established graphical semiology to produce a visual threat assessment language. This language is embodied in widely available GIS software, Google Earth, interoperating with landscape modeling (LM) software, Trimble SketchUp, in order to 1) visualize anthropogenic threats, and 2) consider environmental mitigations for those threats. A prototypical threat modeling application connects the two software applications. The results of this research are threefold. First, a standardized symbology is designed for visualizing anthropogenic threats. Second, this symbology is demonstrated in commonly available GIS software. Third, a framework is established for coupling LM and GIS packages that immediately increases their value in emergency management and response planning

As urbanization has increased in recent decades, urban food systems have become stressed, reducing food security (Gregory, Ingram, and Brklacich 2005). Community gardens that occupy a city's vacant lots have been known to combat food insecurity (Oulton 2012, Colasanti 2009), but many compact cities lack space to garden. One solution has been the development of rooftop gardens (Tian and Jim 2012). In recent decades, Seattle, Washington has increased the number of community gardens, but like many urbanizing centers, the city lacks vacant lots for gardening. With limited ground availability in Seattle and an ever increasing demand to expand upon the city's community garden program, otherwise known as P-Patches, to combat this rapid expansion and improve food security, the city has started to become more creative with its urban spaces through activities such as rooftop gardens (Forbes 2013, Cronin 2013, Greene 2013, Seattle.gov 2014). The goals of this thesis are the following: (1) determine criteria to represent Seattle's food system in a site-suitability analysis to improve food security; (2) rank 33 potential buildings using this spatial index; (3) and perform a ground-truthing exercise to complete and on-site assessment of the seven highest ranked buildings. By taking a more holistic approach when selecting variables, buildings were identified that not only provided the structural needs of a rooftop community garden, but are optimally located within the city's food system based on availability, accessibility, utilization, the three main components that comprise an urban food system (Gregory, Ingram, and Brklacich 2005). Future studies should examine further modification of selecting for these food system variables, which could then provide a more accurate and realistic representation of urban food systems as a means to improve food security.

Increased wildfire activity throughout California over the past decade demands greater research on wildfire management approaches. Understanding natural, as well as human landscape characteristics that explain spatial patterns of wildfire potential can be used to complement traditional wildfire management approaches, such as fire suppression, by identifying high risk areas. In this study, California's wildfire potential was statistically modeled using wildfire observations from a 30-year period (1984 to 2013) and a wide variety of environmental variables. Locations of burned wildland habitat encountered between 1984 and 2013 were related to ignition sources, climate conditions, topography, and vegetation to estimate the probability of wildfire for regions of California exclusive of past wildfire occurrences. Twentynine variables were considered in building the wildfire probability model to determine which factors best indicate environmental susceptibility to wildfires. Two additional models, historic (1984-1988) and recent (2009-2013), were created to assess changes of wildfire probability across California over time. Results of the long-term wildfire probability model display a heterogeneous distribution of wildfire probability across the state. Comparison between recent and historic wildfire probability values demonstrates fluctuations in wildfire potential near coastal and forested areas. Wildfire probability maps depicting the likelihood of wildfire in California can aid land as well as disaster management activities and can enhance the safety of firefighters and the public, and minimize wildland and property damages.

The relationship between culture and urban forests is explored by analyzing residential urban trees within the privately owned residential lots of City of West Covina residents in Los Angeles County, CA. Because the largest percentage of Hispanic immigrants in Los Angeles have historically come from rural, often agriculturally fertile areas in Mexico, urban forest structure was studied to identify possible differences in the management practices of privately owned residential trees in Hispanic neighborhoods; looking for the possibility of increased private urban agriculture. The second largest minority group in the city, Asians, were incorporated into the analysis as the second largest minority group and to compare two sets of results. Object-based image analysis was applied to extract urban forest structure data and OLS regression was employed to explore these relationships. When controlling for several factors like parcel size, property values, and income levels, a statistically significant relationship at the 90% confidence level was found between Hispanic and/or Asian populations and all three dependent variables describing urban forest structure. An inverse relationship between higher tree densities and the height of trees and Hispanic populations was found, however, the coefficients were small. Asian populations were found to have positive associations between all forest structure metrics: a statistically significant and positive relationship was found between large Asian populations, tree density, tree height and urban tree canopy cover. Although results showed some connection between culture and urban forest structure variables, further research and additional methods are needed to explore the validity, strength and complexity of any relationships found.

It is expensive to get elected to the U.S. House of Representatives, and in the past several decades the increase in spending has been very steep. In 2012, candidates spent an average of nearly $1.2 million (Ornstein, et al 2013). However, that includes only direct candidate or party expenditures, and does not included money spent by outside (i.e., "independent") groups. Lessig (2011) argues that the way campaigns are funded, and the dependence members of Congress have on a relatively small number of donors is a form of corruption in our political system. This thesis produces an interactive web map showing the geographic distribution of campaign contributions and independent expenditures made for members of the U.S. House of Representatives. Campaign finance data are most commonly displayed in tables and graphs. They are useful and important for those seeking to investigate the details of campaigns or needing to answer specific question, but a map is more accessible and engaging for the general public. There are numerous other visualizations available on the internet, but many have not been updated since 2012 election cycle (or earlier), or may not include all sources of spending. The web map created as a part of this thesis enables a user to select a candidate and view contributions summed by zip code using graduated symbols. The geographic origin of contributions is apparent, whether within or outside the district. A user can also search for groups that made independent expenditures and see the congressional districts where money was spent. An evaluation of the web map by a small sample of people showed the effectiveness of visualizing campaign finance data to better inform the public about money used in elections.

The increasing threat and globalization of terrorism has heightened the need for estimating the geographical extent of population at risk to terrorist attacks. These estimations provide effective and efficient analyses to support various organizations for estimating necessary aid resources as well as identifying areas that require military and governmental involvement. With no consistent framework available for studying terrorism risk or handling data gaps, the goal of this study is to provide a baseline methodology for spatially estimating population at risk within a data-poor environment (Willis et al. 2005). This thesis examines the Islamic insurgent group, Boko Haram, and their historical attacks within Borno State, Nigeria over a five year period from July 2009 to June 2014. Data is disaggregated using a dasymetric mapping method designed to increase spatial quality to provide a more intimate look at risk throughout the state. Cox Regression, a statistical method to analyze time between events in accordance with covariates' relationships, estimates risk through hazard ratios which are applied to spatial cells. Classified risk cells are used to estimate population at risk in areas through this model. Results depict detailed areas and population at risk to Boko Haram terrorism, the spread of Boko Haram from Borno State to nearby areas over time, and geographic variables which increase odds of Boko Haram attacks to occur. These results are useful to understand the areas and amount of people affected by Boko Haram terrorism and aim to improve methods and techniques using geographic information systems (GIS) and statistical methods for risk analysis. Geographically disaggregating data in data-poor countries provides previously unknown insights to analytical problems potentially facilitating solutions for various subjects such as medical and environmental crises, terrorism, and urban development.

The goal of this research was to assess alternative proposed bridge crossing locations over the Merrimack River between the cities of Nashua, NH and Manchester, NH resulting from two site suitability analysis studies that employ different criteria. A new bridge will provide an alternate route for commuters to access the F.E. Everett Turnpike and U.S. Route 3 in southern NH. Historic traffic count trends show that traffic on bridges and collector roads has increased substantially due to residential growth. This thesis compared alternatives proposed by a site suitability study conducted by Nashua Regional Planning Commission (NRPC) in 2003 to new solutions derived in this thesis through weighted overlay analysis, which took into account distance between major population concentrations and roads, position with respect to historic floodways and terrain, and environmental impacts. The comparison shows how the location of the most suitable bridge locations to span the Merrimack River change when the criteria are altered and different suitability analysis processes are used. The thesis includes a description of criteria and data utilized in the research, an explanation of how the standardized input layers were created, an examination of the methodology for the weighted overlay analysis, and the comparison results of the two site suitability analysis studies.

Millions of birds are killed every year during their annual migration by colliding with tall communication towers and buildings. The goal of this study is to identify areas of specific concern for avian species during migration by modeling potential migration corridors for Red-eyed Vireo (Vireo olivaceus), Kirtland's Warbler (Setophaga kirtlandii), and Golden-cheeked Warbler (Setophaga chrysoparia) as a case study. These avian species perform transcontinental migrations each year. This study uses a least accumulated-cost analysis to predict probability of use of routes between winter and summer ranges by analyzing the presumed energetic cost of changing altitude (in response to topographic relief), traversing large bodies of water, and compensating for wind. Previous descriptions of migration pathways depict straight lines that do not take into account geographic barriers. This study compares the results of existing methods to the least accumulative cost model. The completion of the analysis on Red-eyed Vireo allows the same analysis to be performed on two more rare species, the Kirtland's Warbler and the Golden-cheeked Warbler. The results of this study show that least accumulated cost analyses are a viable option to assisting in determining preferred migration routes for migratory birds. Least accumulated-cost analyses demand significant computing resources, which can prevent studies of this size from being performed. Advances in technology now enable studies of this magnitude to be performed and this study is a proof-of-concept to illustrate the potential benefits of integrating these analyses into conservation planning.

This thesis examines how potential accessibility (A(P)) through transportation over the time span of this study (1830-2010) affected population growth by county for the state of Tennessee. It focuses on shifts in transportation networks from waterways to rail, and rail to roadways, using decennial census data and likewise temporally adjusted county boundaries. The span of this study was broken into four individual time periods to best measure major transitions in transportation: waterways (1830 - 1850), railways (1860 - 1920), historic roads (1930 - 1970), and modern roads (1980 - 2010). Potential accessibility, which was anticipated to have influenced the population change taking place within the state over time, was measured using Esri ArcMap geographical information system (GIS) and a series of network datasets. Calculations of population sums, geographic measurements, and network accessibility were accomplished using both Microsoft (MS) Excel and Esri ArcMap. Linear regression modeling was performed using Statistical Package for the Social Sciences (SPSS). The results suggest that the variable influence was dependent on the study period, and although conclusively correlated at times, other variables in addition to or other than transport accessibility also proved significant in several of the study periods. Specifically, the waterways study period showed a direct correlation with the population growth and transport networks during this time, though additional variables could have contributed to population change as well. The railway network did not significantly contribute to population changes going on during this time, likely directly related to the onset of the civil war which hindered the development and growth of this transport system. While starting population share proved to be significant, with higher growth in counties that started out with larger populations, again additional variables could help explain population growth during the railway study period. Potential accessibility and starting share collectively explained almost 90% of the variance within the historic road model, proving significant and likewise leaving very little of the change in population unexplained during that time period. Oddly, while the potential accessibility was significant, unlike theorized within this study counties less accessible to transport networks actually grew more quickly than those with higher accessibility. Finally, modern roads were found to be significant in population change as well and highly correlated. Additional steps to improve on this study in the future would include considering connections outside of the state, particularly in non-Tennessee peripheral localities with high populations. Secondly, investigation of additional variables such as economic data over a shorter overall time span, or using dasymetric allocation methods, could also provide further explanation behind population changes taking place over time.

This thesis is a cartographic history of St. Augustine, Florida. In crafting the maps, I was struck by the remarkable persistence of a 4-block area just north of the center of town known as the Minorcan Quarter. Research revealed interesting cultural background and economic conditions in this area, but the persistence of these conditions lacked explanation. Although substantially reshaped by tourism and commercialism in the late 20th century, the Minorcan Quarter is still an identifiable neighborhood of St. Augustine today. The theory of institutional inscription (Ethington, 2011) holds that cultures leave definitive imprints in the regions where they are established; its foremost application has been to the Los Angeles Basin of California. Here, I apply this theory to the much smaller area of St. Augustine, particularly the Minorcan Quarter, to demonstrate and quantify the institutional imprints at the block and sub-block levels during successive periods of Spanish, British, and American settlement and occupation over a 250-year period (1764-2013). In this research, I am aided by remarkable maps crafted by an early 20th century cartographer, Ramola H. Drost, whose compilation made up for limitations inherent in census-related information during the time periods of this study. A quantified rating system for the persistence of institutional inscription is introduced and demonstrated within the Minorcan Quarter of St. Augustine. This metric, which takes into account both land parcel configuration and land use, varies only from 5 to 7 (on a ten-point scale) within the quarter. By comparison, persistence ratings of 3 and 9 apply to the late 19th and 20th century subdivisions to the southwest and the imposing fortress to the northeast of the quarter. A cartonomicon, including a transsection diagram (after Ethington) and a time-ring chart (this work), is developed to analyze and explain the persistence of the Minorcan Quarter in St. Augustine, Florida.

Ever-increasing demand on Earth's finite natural resources and land requires environmental planners to employ informed and successful management of environments. Historical resources enhance environmental management by providing information to compare past landscapes to contemporary, urbanized states. In this study, heterogeneous historical resources were converted into GIS datasets to reconstruct the Ballona Creek watershed in Los Angeles, California as a three-dimensional (3D) model. To develop the 3D terrain, contour lines were extracted from early 20th century United States Geological Survey (USGS) topographic maps. Transforming contour lines into a Digital Elevation Models (DEM) enabled creation of 3D models to visualize the terrain of the Ballona Creek watershed before the region was heavily urbanized. To increase the effectiveness and functionality of these models, 3D vegetation and hydrography features were also added to the terrain to "paint a picture" of the historic extent of the Ballona Creek watershed. The historic 3D topography allowed calculation of elevation changes occurring over the last century to the Ballona Creek watershed and provided visualizations of previously reconstructed historical habitats. These visualizations and associated analyses comparing historic and current conditions provide a historical perspective for environmental planners to identify landscape changes and current trajectories of urbanized landscapes. These results suggest that 3D visualizations models, synthesized from an array of historical resources, can effectively deliver information about past landscapes to environmental planners, decision makers, and the public.

Small scale farming identify farms with less than 300 acres of agricultural land and represent a large population of producers in the US, thus the interest in procedures such as Precision Agriculture Application in productivity cycles. This study compares publically available Landsat7 ETM+ imagery, at nominal 30 meters pixel resolution, and National Agricultural Imagery Program's (NAIP) imagery, at nominal 1 meter pixel resolution, to evaluate their use in Precision Agriculture (PA) applications for small-scale farming. The selected study area was determined based on crop characterization and land size criteria identified in the South Eastern part of Pittsylvania County, VA. The selected agricultural fields within the study area, 14 in total, were of varying shapes, ranging from 7.5 to 150 acres in size, and characterized by a specific crop type such as non-alfalfa hay. The methodology for this study consisted in the computation and analysis of four vegetation indices (VIs) to evaluate the effect of imagery resolution to depict vegetation maturity in the selected 14 sites. The VIs used consisted of: Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Soil-Adjusted Vegetation Index (SAVI). In addition to the Vis analysis, a pixel Percent Error estimate was derived from the low and high resolution VIs products to evaluate the amount of variance between Landsat7 ETM+ and NAIP data. As expected, NAIP's VIs results provided more detail about the study sites compared to the Landsat7 ETM+ VIs products. This was evident as NAIP's ability to locate and visualize vegetation and non-vegetation features within the study sites, which is of particular importance for PA applications. In contrast, Landsat7 ETM+ imagery were not able to provide adequate identification and monitoring capabilities when used in limited areal extent, specifically required for small scale farming PA applications. Spectral mixing of land features smaller than the 30 meters pixel resolution imagery were causing vegetation differences to be diluted across the fields rather than being isolated and identifiable like in the NAIP's VIs results. Results from the PE analysis confirm the VI results and show a great difference between VI values derived from the low resolution Landsat7 ETM+ and high resolution NAIP imagery. The majority of the sites contain a high percentage of pixels error above the acceptable percentage, which outline that VI values derived from low resolution imagery do not provide results comparable to the high resolution imagery. Moreover, the size of the sites do have an effect on the amount of acceptable PE within each field, with larger fields containing higher percentages of Acceptable PE than smaller sites. Therefore, due to the use of reduced size fields in small scale farming, the use of low resolution imagery might not be appropriate to adequately represent the actual ground conditions necessary for reliable PA use.

The goal of this thesis is to complete a raster-based site suitability analysis that identifies varying levels of need for financial education in the City of Malden Massachusetts. The research product is intended to help decision makers evaluate where to site financial education training events or invest in support programs for the communities or neighborhoods in need. The thesis begins by reviewing the current state of financial education and arguments for its application. Individual factors and supporting evidence that seek to identify individuals in need of financial education is organized. The data preparation steps and multi-criteria evaluation (MCE) spatial methodology used to create the final research output is detailed. The MCE results in the City of Malden identify multiple regions with a high level of need for financial education. A review of these high need areas coincident with city zoning and a variety of geographic features highlights additional spatial relationships of interest. The author concludes the research by outlining how the final output can support deciding on locations for financial education events.

Multiple events throughout the history of the United States of America have led people to call for the Electoral College system to be reformed or abandoned altogether. As the Electoral College currently functions, each state awards a set number of votes determined by population) to the candidate who receives the largest number of votes, but many citizens feel that there are flaws in this system. Although there have been many reform propositions over the years, there are three potential methods that consistently have the most support: Popular Vote, Proportional Allocation, and Congressional Districts Allocation. This study offers insight into how each of these reform methods might change election outcomes and even more importantly, by exploring several possible election mapping techniques, it provides an analysis of how the presentation of election results in a geographic format can alter the viewer's perceptions of election outcomes and of the viability of the various reform methods. Finally, this study provides arguments for why the traditional methods of representing election outcomes tend to fall short.

As carbon based fossil fuels become increasingly scarce, renewable energy sources are coming to the forefront of policy discussions around the globe. As a result, the State of Hawaii has implemented aggressive goals to achieve energy independence by 2030. Renewable electricity generation using solar photovoltaic technologies plays an important role in these efforts. This study utilizes geographic information system (GIS) and LiDAR with statistical analysis to identify how much solar photovoltaic potential exists for residential rooftops in the town of Kailua Kona on Hawaii Island. This study helps to quantify the magnitude of possible solar PV potential on residential rooftops within the study area. Three main areas were addressed in the execution of this research: (1) modeling solar radiation, (2) estimating available rooftop area, and (3) calculating PV potential from incoming solar radiation. Esri's solar modeling tools and high resolution LiDAR data were utilized to calculate incoming solar radiation on a sample set of digitized rooftops. Photovoltaic potential for the sample set was then calculated with the equations developed by Suri et Al. 2005. Sample set rooftops were analyzed using a statistical model to identify the correlation between rooftop area and lot size. Least squares multiple linear regression analysis was performed to identify the relationship between the slope, elevation, rooftop area and lot size explanatory variables and their influence on the modeled PV potential values. The equations built from these statistical analyses of the sample set were applied to the entire study region to calculate total rooftop area and PV potential. One parcel with real time PV production data was chosen for a ground truth comparison. This ground truth served as a means to evaluate the performance of the rooftop area calculations and the PV potential estimation methods. The total study area statistical analysis findings estimate photovoltaic electric energy generation potential for rooftops is approximately 190,000,000 kWh annually. This is approximately 17% of the total electricity the utility provided to the entire island in 2012. Based on these findings, full rooftop PV installations on the 4,460 study area homes could provide enough energy to power over 31,000 homes annually. Results from the ground truth comparison show the PV modeled values to be approximately 68 percent of actual PV production on the ground truth site. This work addresses a significant lack of scientific research regarding solar PV potential in the study area. The methods developed here suggest a means to calculate rooftop area and PV potential in a region with limited available data. This effort could be effectively replicated in other areas. This study also provides a launching point future studies addressing the larger issues associated with net energy metering capacity, grid stability and saturation as well as a growing need for a better understanding of the factors that influence solar PV potential.

Communication and information technology are critical in facilitating the processes in which public health stakeholders understand and utilize health information. Spatial visualization enables public health practitioners to effectively present geographic phenomena and detect spatial patterns in maps that may remain otherwise undiscovered in tabular form. Although there are many public health practitioners integrating spatial visualization into their work, there are few resources dedicated to instructing how to best visualize health data. Mapmakers will find that, among the wealth of resources on cartography and visualization best practices, few are specific to how health data can be best spatially visualized. Communication of such data is critical in understanding public health issues and developing prevention and intervention programs. This study aimed to 1) document best practices for visualizing public health data using thematic mapping techniques and 2) demonstrate how spatial visualization can be integrated into public health studies to facilitate understanding and communication of findings. A process for identifying suitable thematic mapping techniques for public health studies is discussed, in addition to best practices for employing such techniques, which includes choropleth, proportional symbol, dot density, and nominal point mapping techniques. A case study is presented to demonstrate how spatial visualization can be successfully integrated into public health studies; sociodemographic risk factors of uninsurance were identified using principal component analysis and later mapped using choropleth mapping best practices. Best practices for visualizing health outcomes, social determinants of health, and health care access, key areas of concern in improving public health, are also provided. This study addresses the gap in cartographic resources for the public health industry and aids public health practitioners in their ability to spatially visualize their data and improve communication of their findings.

Global Land Survey (GLS) data encompassing Landsat Multispectral Scanner (MSS) Landsat 5's Thematic Mapper (TM) and Landsat 7's Enhanced Thematic Mapper Plus (ETM+) were used to determine the terminus locations of Baird, Patterson, LeConte, and Shakes Glaciers in Alaska and investigate the movement rates of these glaciers with respect to specific physical and environmental conditions.GLS data from 1974, 1989, 1999, 2005, and 2009 in false-color composite images enhancing ice-snow differentiation and Iterative Self-Organizing (ISO) Data Cluster Unsupervised Classifications were used to 1) quantify the movement rates of Baird, Patterson, LeConte, and Shakes Glaciers; 2) analyze the movement rates for glaciers with similar terminal terrain conditions and; 3) analyze the movement rates for glaciers with dissimilar terminal terrain conditions. From the established sequence of terminus locations, movement distances were quantified between the glacier locations. Movement distances were then compared to see if any correlation existed between glaciers with similar or dissimilar terminal terrain conditions. The Global Land Ice Measurement from Space (GLIMS) data was used as a starting point from which glacier movement was measured for Baird, Patterson, and LeConte Glaciers only as the Shakes Glacier is currently not included in the GLIMS database. Results show that glaciers with similar terminal terrain conditions (Patterson and Shakes Glaciers) and glaciers with dissimilar terminal terrain conditions (Baird, Patterson, and LeConte Glaciers) did not exhibit similar movement rates. Glacier movement rates were greatest for glaciers whose terminuses were in fresh water (Patterson and Shakes Glaciers), less for those with terminuses in salt water (LeConte Glacier), and least for glaciers with terminuses on dry land (Baird Glacier).

Global Land Survey (GLS) data encompassing Landsat Multispectral Scanner (MSS) Landsat 5's Thematic Mapper (TM) and Landsat 7's Enhanced Thematic Mapper Plus (ETM+) were used to determine the terminus locations of Baird, Patterson, LeConte, and Shakes Glaciers in Alaska and investigate the movement rates of these glaciers with respect to specific physical and environmental conditions.GLS data from 1974, 1989, 1999, 2005, and 2009 in false-color composite images enhancing ice-snow differentiation and Iterative Self-Organizing (ISO) Data Cluster Unsupervised Classifications were used to 1) quantify the movement rates of Baird, Patterson, LeConte, and Shakes Glaciers; 2) analyze the movement rates for glaciers with similar terminal terrain conditions and; 3) analyze the movement rates for glaciers with dissimilar terminal terrain conditions. From the established sequence of terminus locations, movement distances were quantified between the glacier locations. Movement distances were then compared to see if any correlation existed between glaciers with similar or dissimilar terminal terrain conditions. The Global Land Ice Measurement from Space (GLIMS) data was used as a starting point from which glacier movement was measured for Baird, Patterson, and LeConte Glaciers only as the Shakes Glacier is currently not included in the GLIMS database. Results show that glaciers with similar terminal terrain conditions (Patterson and Shakes Glaciers) and glaciers with dissimilar terminal terrain conditions (Baird, Patterson, and LeConte Glaciers) did not exhibit similar movement rates. Glacier movement rates were greatest for glaciers whose terminuses were in fresh water (Patterson and Shakes Glaciers), less for those with terminuses in salt water (LeConte Glacier), and least for glaciers with terminuses on dry land (Baird Glacier).

Global climate models (GCMs) allow geologists to test physical explanations for the formation and modification of climate-related features on planetary bodies with an atmosphere. This method of analysis depends upon two data sources: the GCM itself and a catalog of features under investigation. A proof-ofconcept analysis is tested on gullies (small erosional channels) and their formation by means of climatechange conditions identifying the transient presence of liquid water, over time, on Mars southern hemisphere. The end-to-end GCM/data integration pipeline includes three primary components: (1) generation of a file geodatabase with coded domains of all imaged gullies in the southern hemisphere of Mars from the Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) images (2) incorporation of three GCM simulations into ArcGIS that quantitatively predict surface conditions over one martian year under three different starting conditions, and (3) the integration of the geodatabase and GCM simulations to create dynamic visualizations of surface conditions over time with respect to gullies along with the quantitative extraction of temperature/pressure values at gully sites to test whether or not liquid water could be transiently stable at these locations. Results show that the formation of gullies by liquid water is unlikely under present atmospheric conditions at most locations, but is predicted to have occurred under more favorable orbital scenarios thought to have occurred in the recent geologic past of Mars. If these correlations are valid, this increases the potential of primitive biology having existed in the recent history of Mars. More broadly, this technique represents a potentially valuable tool within a GIS environment for increasing confidence in data/model comparisons at global, hemispheric and regional scales.

Online geospatial data are evident in many websites, covering a variety of interests such as route planning, incident locations, and outdoor recreation searches. One type of geospatial website is the online real estate search. Many realty websites allow prospective residential property buyers to sort listed properties interactively based on desired elements. These elements typically address features wanted within a home, such as the dwelling's size, number of bedrooms and bathrooms, whether a garage or swimming pool is included, and other furnishings. Equally important in considering the ideal home is to find the ideal location. Length of the commute time, crime frequency, proximity to cultural and retail options, and the location of desired schools can provide for an overall "neighborliness" that is vital to ensure a comfortable life in the new home at the new location. While some websites are beginning to address this concern by including small overlays within a property's webpage, none overtly considers that the home buying process may not start with the selection of home features, but by first determining the "best-fit" neighborhood. The web application created for this thesis is unique in its premise to first introduce potential homebuyers to neighborhoods. Prospective homebuyers may select from several neighborhood factors to find locations that satisfy their search parameters. An overlay of available properties is then displayed for the web application user to show what offerings are available in those resulting areas.

Rapid and accurate diagnoses are important because they drive evidence-based care in health systems. Point-of-care technologies (POCT) can aid in diagnosis by bringing advanced technologies out of hospital or clinical settings and closer to the patients. Health networks are constrained by natural connectivity in the interactions between geography of resources and social forces. Using a geographic information system (GIS) we can understand how populations utilize their health networks, visualize their inefficiencies, and model alternatives. This project focuses on cardiac care resource in rural Isaan, Thailand. A health access model was created using ArcGIS Network Analyst 10.1 from data representing aggregated population, roads, health resource facilities, and diagnostic technologies. This model was used to quantify current cardiac health access and improve upon that access using both widespread and resource limited strategies. Sensitivity analysis revealed that altering travel speeds of roads has a large effect on the calculation of health access. Results indicated that having diagnostic technologies closer to population allowed the streamlining of care paths. The model allowed for comparison of the effectiveness of the implementation strategies. This model was created to help put the benefit of adopting POCT within health networks within perspective. Additionally it can help evaluate these alternatives diagnostic placement strategies as compared to the current health access and evaluate the relative costs and benefits.

An estimated 11.7 million unauthorized immigrants resided in the United States in 2012 according to the Pew Hispanic Center (Passel, Cohn, and Gonzalez-Barrera 2013). Reforming the U.S. immigration system is a clear policy priority for President Barack Obama, and an agenda item for the 113th Congress (U.S. Congressional Research Service 2013). Based on prior legislation, processing of immigrants for legalization is likely to be a complex and time consuming task, necessitating the involvement of nonprofit and public infrastructure. The goal of this study was to design a research methodology for estimating the unauthorized population at the census tract level, as a means for visually representing the relative densities of the unauthorized population in a way that would be useful for planning where to provide services for the unauthorized populations within a community. Using statistical methods, the relationships between the dependent and independent variables was defined at the state level. The state level relationships were then applied to census tract level data in order to make census tract estimates. The results of the analysis were displayed as relative densities using the dot density renderer in ArcGIS Desktop. The performance of this model was verified by comparing the results generated in this study to those of other studies. Based on this verification method, the performance of the model varied by geography, with the western states, in particular, California seeming to have performed the best. The states that appear to have performed the worst are primarily located in northeastern United States and include six out of the eight states with the lowest number of unauthorized persons (

Understanding the geographic distribution of insurgency violence is critical for assessing where counterinsurgency and civilian protections operations are effective. It allows researchers and policymakers to detect trends in violence and propose local programs designed to quell insurgency aggression in vulnerable areas. This study examines the spatial distribution of armed-conflicts in Central Africa committed by the Lord's Resistance Army from 2008 to 2012 and offers a quantitative evaluation regarding the fluctuation of violence throughout the region. Existing counter-insurgency programs are discussed, and additional analysis is performed on the development of a high-frequency radio network designed to facilitate information sharing between communities. Resulting geographic representations indicate a steady decline in armed-conflicts in the Democratic Republic of the Congo and South Sudan with violence becoming more prevalent in the Central African Republic. Findings deliver important insights to a growing body of research exploring the evolution of violence and the capacity for counterinsurgency efforts in a region rich with resources yet afflicted with deep-rooted ethnic and ideological differences.

To alleviate some of the environmental and traffic concerns caused by the growth around the Ports of Los Angeles and Long Beach, research has begun on the establishment of a large inland port. An inland port (or dry port) stores cargo, transfers containers from drayage trucks to rail, and largely shifts seaport activities off-site (Roso, Woxenius, and Lumsden 2008). A location-allocation analysis has been conducted for the Los Angeles region to determine potential sites for an inland port in terms of distance from the seaports and reduction in vehicle miles traveled (truck VMTs) (Rahimi, Asef-Vaziri, and Harrison 2008). This study builds on this research by conducting a site suitability analysis (SSA). First, the study pre-screens numerous parcels for size and rail line proximity to limit the analysis to viable sites. Next the study investigates key siting decision variables in greater detail. These include rail line feasibility, parcel acreage, distance from schools, population density, and total truck VMT reduction. Using Arc GIS, the data were analyzed and transformed into scores to sum site desirability based on an even weighting of these criteria. Data for this study were obtained from multiple geographic information system (GIS) data warehouses including, Census.gov, egis3.lacounty.gov, ArcGIS Online, and the State of California Geoportal website. This study reaches three main conclusions. First, a site suitability analysis is needed when it comes to analyzing a multitude of variables and selecting a proper site for an inland port in an urban setting. Secondly, there are possible sites where an inland port can be placed and connected via rail that will minimize overall truck VMTs in the region. Lastly, although many sites scored high on some of the criteria, no one site stands out as optimal, according to the criteria chosen, for a heavy industrial facility, such as an inland port, in the Southern California region.

Since 2001, temporary flight restrictions (TFRs) have existed around professional baseball games, professional football games, college football games, and major races; these TFRs are commonly referred to as the sporting event TFRs. Neither the Federal Aviation Administration's official TFR map nor the most commonly used aviation information website display the locations of these TFRs. Furthermore, an official, FAA sponsored preflight briefing does not contribute to pilots' awareness of the location or timing of these sporting event TFRs. In fact, the burden of knowing where, when, and for how long each NFL, MLB, NCAA division 1 football, NASCAR, and IndyCar event will take place is placed squarely on the shoulders of all pilots. This thesis reports on the creation of a web map specifically geared towards providing pilots with the locations and times of all sporting event TFRs in the United States. This data, as a database and a web service, has been offered to the FAA and several established, popular aviation websites to encourage display of these important TFRs on their web maps.

This project sought a method to map Sudden Oak Death distribution in the Santa Cruz Mountains of California, a coastal mountain range and one of the locations where this disease was first observed. The project researched a method to identify forest affected by SOD using 30 m multi-spectral Landsat satellite imagery to classify tree mortality at the canopy-level throughout the study area, and applied that method to a time series of data to show pattern of spread. A successful methodology would be of interest to scientists trying to identify areas which escaped disease contagion, environmentalists attempting to quantify damage, and land managers evaluating the health of their forests. The more we can learn about the disease, the more chance we have to prevent further spread and damage to existing wild lands. The primary data source for this research was springtime Landsat Climate Data Record surface reflectance data. Non-forest areas were masked out using data produced by the National Land Cover Database and supplemental land cover classification from the Landsat 2011 Climate Data Record image. Areas with other known causes of tree death, as identified by Fire and Resource Assessment Program fire perimeter polygons, and US Department of Agriculture Forest Health Monitoring Program Aerial Detection Survey polygons, were also masked out. Within the remaining forested study area, manually-created points were classified based on the land cover contained by the corresponding Landsat 2011 pixel. These were used to extract value ranges from the Landsat bands and calculated vegetation indices. The range and index which best differentiated healthy from dead trees, SWIR/NIR, was applied to each Landsat scene in the time series to map tree mortality. Results Validation Points, classified using Google Earth high- resolution aerial imagery, were created to evaluate the accuracy of the mapping methodology for the 2011 data.

Precision agriculture is a rapidly developing set of technologies that aids management decisions in agricultural entities. Fertility and lime management is directly impacted by precision agriculture through the application of variable-rate technology (VRT). This allows for the rate of application of one or more materials to be adjusted based on positioning information and predetermined application rates. The basis for VRT is soil sampling. In this study, multiple precision agriculture grid and zone-based soil sampling methods and procedures are utilized on a farm in northeastern North Carolina. The results from these soil sampling methods are evaluated against the results of a "gold" standard sampling method. The findings will potentially begin to determine one or more best suited soil sampling methods for northeastern North Carolina, while also potentially eliminating ineffective ones.

Much of northern Minnesota is underlain by rocks that make up the so-called Superior Province of the Canadian Shield -- the ancient core of the North American continent. These Superior Province rocks originated in the Archean Eon, between 4.0 and 2.5 billion years ago. Altogether, more than 60% of Earth's crust formed during this time period, making Archean terranes worldwide particularly rich in mineral resources. Even among other Archean terranes, the Superior Province is exceptionally rich in gold. The Canadian province of Ontario, immediately north of Minnesota, is host to over 300 significant gold deposits, with 18 currently producing mines. However, no economically significant gold deposit has yet been discovered in Minnesota, despite several periods of intense exploration activity in the 1980s and early 1990s. This project utilized public datasets representing geology, geophysics, and geochemistry to predict the likelihood of new gold occurrences in northern Minnesota's Archean bedrock, using a geospatial information systems (GIS) modeling technique called weights of evidence. The study area was ranked on a relative scale from low gold potential (Not permissive) to high gold potential (Favorable). Comparison of model results to past and present gold exploration activity suggests that weights-of-evidence modeling is a useful tool for generating new exploration targets in northern Minnesota. Many of the tracts ranked as Favorable do not appear to have ever been drilled, so the bedrock in these areas has yet to be evaluated. Also, because most of the datasets used were either first published or significantly updated between 2004 and 2012, this project is likely the first to use them in a predictive model, and offers a new perspective on gold prospectivity in the region.

Earthquakes strike without warning and leave a trail of devastation. To better prepare for these disastrous events, government agencies must have a comprehensive emergency management plan based on current spatial and non-spatial data. Applications such as HAZUS-MH, developed by the Federal Emergency Management Agency (FEMA), can be used with ArcGIS software to model loss estimations for many natural disaster scenarios. However, HAZUS-MH does not supply the necessary data to analyze losses at geographic units smaller than the census tract level, limiting its effectiveness for an urban area earthquake casualty study. Focusing on the Central Business District (CBD) of Lexington (Kentucky), this study developed a new methodology to test alternate input such as locally sourced LiDAR remote sensing data and Geographic Information System (GIS) -based parcels data to predict earthquake casualties within an urban area. The Urban Daytime Seismic Casualty Estimation (UDSCE) method was applied at a census tract level and casualty estimations validated using the HAZUS-MH model results from three simulated earthquake scenarios. The UDSCE methodology was then applied at the census block and parcel level to refine estimates counts at higher resolution. The results show compelling evidence that working at the census block and parcel level can provide focalized casualty counts within the urban context, thus providing emergency planners crucial information to better prepare for earthquake events in commercial/urban densely populated areas.

In 2000 the United Nations (UN) created the Millennium Development Goals (MDGs) to focus on addressing major issues like poverty, education, children's health, sustainable environment, disease prevention, and economic development. One of the targets (7C) of the MDGs is to halve the portion of the population that does not currently have sustainable access to safe drinking water and basic sanitation by 2015. As a region, sub-Saharan Africa is not on track to meet the goal. In fact, the region has the lowest clean drinking water coverage of any region in the world. This project develops a general framework to improve water resource planning in sub-Saharan Africa. The project defines criteria, data and methods to improve planning for clean drinking water wells. The result is a general framework for 1) finding locations where contamination of water wells is least likely to occur, and 2) ensuring the benefits of clean water support overall community health and education. This is all with the aim to increase efficient water resource planning to support the MDG to increase safe drinking water coverage. The general framework is implemented as a model which is the functional component of the framework. The general framework was refined through the implementation of the model in a model fitting study in rural Uganda. The result of the implementation is a suitability map identifying locations where (1) risks to drinking water are minimized and (2) benefits to people living in the study area are maximized. The success of the model was evaluated by assessing the locations of existing wells against what the model identified as suitable well locations. The framework and model fitting process can be used as a tool by governments and non-government organizations (NGOs) to improve current water site suitability workflows.

Strix occidentalis lucida (Mexican spotted owl) is a threatened wildlife species under the provisions of the Endangered Species Act (ESA) and in recent years Gila National Forest (GNF), New Mexico has been a vital stronghold in providing suitable habitat for remaining owl populations. Historical point call survey data provided by the U.S. Forest Service (USFS) was processed to generate 405 presence points, which were used to generate 405 pseudo-absences. For modeling purposes, 75% of the 405 presence and absence points were used for training habitat suitability models and 25% were set aside for validation. Maxent and logistic regression were the methods selected for modeling Mexican spotted owl habitat suitability. Several topographic, water resource, vegetative, and climatic environmental variables were selected as the potential environmental predictors. A stepwise Maxent model included the variables land surface temperature low pass (lst low), elevation, and stream proximity (sprox), resulting in a validation kappa of 0.370 and AUC of 0.777. The best logistic regression model consisted of lst low, elevation, stream proximity, modified soil adjusted vegetation index (msavi), and slope as the environmental variables with a validation kappa of 0.267 and AUC of 0.750. Maxent and logistic regression habitat suitability models had poor agreement when assessed using the habitat suitability classes; however, they agreed substantially when comparing total suitable habitat with a kappa of 0.655. The habitat suitability models both performed well, gave similar accuracies, and may possibly aid future Mexican spotted owl surveys within GNF.

This study illustrates how GIS technology can help to show how social capital is generated and why firing ranges, specifically, can help to generate social capital. The term social capital was defined by Hanifan (1916) as the tangible assets most significant in daily life, such as goodwill, fellowship, and sympathy. This study builds upon previous social capital research by the Organization for Economic Co-operation and Development (OECD), the World Bank, Bettertogether.org, and Robert Putnam. It uses GIS mapping and analysis tools to chart the spatial relationships between firing ranges and traditional social capital generating sources in the Greater Houston, Texas area. A survey modeled after the World Bank's Social Capital Assessment Tool (SOCAT) and Harvard's Social Capital Community Benchmark Survey (SCCBS) was distributed to five firing ranges in Houston, Texas and the results of the survey were used to map the locations of firing ranges and patrons and to show firing ranges help to create social capital in the same manner sporting venues, religious, and community institutions do. This study illustrates why firing ranges are analogous to known social capital generators in their ability to create social capital, assesses the need to employ GIS technology in continued research on the social capital landscape created by non-traditional sources such as firing ranges in America, and marks an opportunity to revisit previous research on social capital from traditional sources and organizations.

Anthropogenic climate change, growing populations, the decrease of essential resources, and the availability of funding to deal with these emerging conditions, provide the incentives for cities to mitigate and adapt through urban sustainability programs. Though web GIS applications visualizing features of sustainability do exist, few visualize actual sustainability indicators, and almost none visualize performance on the refined scale of the city. A web GIS application targeting such objectives with urban sustainability indicators was developed for Oakland, California. The application demonstrates a tool for planners and the public by creating a starting point for a time-referenced spatial view for the pace of progress. The six broad indicator elements determined by the city of Oakland's Annual Sustainability Report worked as the foundation to customize spatially related indicators meeting specifications of quality in representation and function. These customized indicators are climate change vulnerability, employment availability, housing, public transit accessibility, natural resource project inventory, as well as culture and community. Another application with editing capabilities informs the culture and community indicator with volunteered geographic information (VGI). The features demonstrated in the applications' functions include classifying methods of performance, a strategy-based approach informed with municipal policy, access to indicator attributes, as well as basic map capabilities allowing for zoom to neighborhood, toggling of individual indicator visibility, and an integration with social media resources. An overview of the steps in the application development process was documented. The application was made available for testing with a survey for feedback that was both utilized and acknowledged for future considerations

The Sun is the center of our galaxy and its patterns have been studied by civilizations since the beginning of time. Solar energy is a complex phenomenon that is the basis for life on Earth. Understanding the position of the Sun during the day is critical for evaluating how its energy impacts our daily lives. In an urban environment, the Sun's energy can be considered as a service as well as a burden. Solar energy is beneficial when it can be harnessed using solar collectors for electric generation or when it contributes to heat energy with passive heat gains in the winter. However, solar energy can cause unwanted heat gains during warm summer months when buildings are trying to keep occupants cool. Solar radiation models used to evaluate favorable conditions and locations have traditionally only required two-dimensional data for evaluation of terrain and rooftops. However, in order to attempt a comprehensive assessment of solar radiation effects with a built environment, three-dimensional data must be used to evaluate vertical surfaces as well. The proposed research can be used to find the links between building energy usage and effects of spatial factors resulting from a building's location. The investigation is centered on an educational building, Lewis Hall, located on the University Park campus of the University of Southern California. The impacts of solar energy evident in the following research should be considered when evaluating and designing efficient building energy systems in the future.

The growing accessibility of mobile phones in developing countries has led to increased innovation and utilization of handheld technology in managing health outcomes. Mobile health (mHealth) technologies enabled significant gains in localized data collection methods and increased timeliness in disease surveillance and control programs. Mobile technology has become an important tool for point of care productivity and effective task shifting for Community Health Workers (CHWs) in many developing countries. Concurrently, GIS technology has increasingly been utilized in public health research, planning, monitoring, and surveillance within many developing countries and low-resource settings. This has resulted in opportunities for better understanding of spatial variation of diseases and the correlations with environmental factors. To better understand community needs and burden of illnesses managed by CHWs, a geospatial analysis at the sub-district level was performed on CHW catchment area health data registries. Risk assessments and cluster analyses were conducted to identify high incidences of fever related illnesses for malaria, diarrhea, and pneumonia in community areas within the rural district area of Chongwe, Zambia. Seventy CHWs recorded 7,674 cases over a time-period of ten months, of which 3,130 cases were geocoded for geospatial analyses. One hundred forty-one village areas within 15 rural health center catchment areas were geocoded and mapped. Results were used to create thematic maps illustrating disease distribution and risks for malaria, pneumonia, and diarrheal illnesses for each subdistrict village area manage by CHWs. The use of mobile technology integrated with GIS to manage community health data and the application of GIS to analyze community level data may provide further insight into local area disease distribution, variability, and community needs than systems focused solely on district level data analysis and lacking GIS integration.

Purchasing land for the use of open space is crucial for municipalities who are concerned with land conservation and urban sprawl mitigation. Ecological contributions from natural resources add to the benefits of parcel purchase. Land resource quality can be quantified by overlaying ecological spatial data into a multiple criteria Geographic Information Science (GIS) environment. Each data input is assigned a level of priority decided upon by city planners. The parcel with greater than average natural resource further explains the priority of parcel acquisition for open space and the economic tradeoff under budget constraints. This research includes identifying future hot spots for land acquisition through an agent-based Modeling Open Space Acquisition (MOSA) model. MOSA is a scientific approach that assists in classifying the ecological resource value of available land for open space acquisition. Within the MOSA model data are ranked by weighted criteria across a sample area of interest. The weighted sum tool in MOSA considers wildlife habitat, agricultural lands, historical sites, recreation corridors, vegetation biodiversity, riparian wetlands, parcel proximity, parcel size, as parcel carrying capacity by weighted criteria. While other ecologically weighted models primarily use discrete vector data, shapes with defined boundaries, MOSA uses the grid value of raster data which is its equivalent in digital pixels. Each pixel in the raster data set represents 50 square feet in the sample area that numerically translate the parcel's ecological resource value. The MOSA model identifies areas of highly natural resourced land as supplemental evidence in quantifying or targeting and prioritizing parcel acquisition for open space preservation. Municipalities, counties, and government agencies can benefit from MOSA where land acquisition is evaluated from a scientific classification of natural resource capital. The ecological land value found in this study is of course highly sensitive, {if one considers their land more economically sound with the spatial information found within), and is intended for the sole purpose of quantifying land ecologically for the purpose of open space acquisition, preservation of public lands, and ultimately mitigating urban sprawl. This research includes sample areas of Boulder, Colorado as a case study.

The San Francisco Public Art Map (SFPAM) mashes up disparate sources of data to create a dynamic, comprehensive, and interactive map of public art and landmark buildings in the City of San Francisco. The San Francisco Arts Commission administers publicly funded art and is responsible for over 800 pieces but maps are incomplete or inaccurate. There are hundreds of other art pieces such as murals, street art, and art funded by private organizations not included in the San Francisco Arts Commission dataset. Existing applications focus on one type of art or a narrow selection of installations. No application combines institutional data sets, peer-reviewed volunteered geographic information, and social media to create a comprehensive view of publicly available art. The SFPAM consists of a web client and ArcGIS Online maps. The web client uses JavaScript, Dojo, social media application programming interfaces, ArcGIS Server, ArcSDE, REST services, and Microsoft SQL Server technologies. Configuration and development to add functionality to Esri's Public Information Map 2.0 source code transformed a disaster map to an art map. The web application incorporates Stamen Design basemaps to provide a fresh look that complements the art content. ArcGIS Online maps enable users to contribute new art and buildings and view art data on mobile devices through the ArcGIS for Mobile or Collector applications. There are three levels of curated data: institutional, administrative, and social. Institutional curation consists of datasets provided by institutions that administer or fund public art projects. The administrative level includes reviewed volunteered geographic information. Social curation consists of a dynamic layer of pictures and comments on public art through social media streams such as Flickr, Panoramio, Instagram, and YouTube. The application demonstrates a unique method of combining data sources to provide a public art map for visitors and residents of San Francisco.

The Salmon Creek Watershed in Sonoma County, California, USA, is home to a variety of wildlife, and many of its residents are mindful of their place in its ecology. In the past half century, several of its native and rare species have become threatened, endangered, or extinct, most notably the once common Coho salmon and Chinook salmon. The cause of this decline is believed to be a combination of global climate change, local land use, and land cover change. More specifically, the clearing of forested land to create vineyards, as well as other agricultural and residential uses, has led to a decline in biodiversity and habitat structure. I studied sub-scenes of Landsat data from 1972 to 2013 for the Salmon Creek Watershed area to estimate forest cover over this period. I used a maximum likelihood hard classifier to determine forest area, a Mahalanobis distance soft classifier to show the software's uncertainty in classification, and manually digitized forest cover to test and compare results for the 2013 30 m image. Because the earliest images were lower spatial resolution, I also tested the effects of resolution on these statistics. The images before 1985 are at 60 m spatial resolution while the later images are at 30 m resolution. Each image was processed individually and the training data were based on knowledge of the area and a mosaic of aerial photography. Each sub-scene was classified into five categories: water, forest, pasture, vineyard/orchard, and developed/barren. The research shows a decline in forest area from 1972 to around the mid-1990s, then an increase in forest area from the mid-1990s to present. The forest statistics can be helpful for conservation and restoration purposes, while the study on resolution can be helpful for landscape analysis on many levels.

Park planners make long-term land acquisition and capital improvement plans based in part on population growth and gap analysis of existing facilities. This study demonstrates a new cadastral-based technique to measure park access for residents in Wake County, NC. Based on road network and cadastral data, the technique uses the Origin-to-Destination Matrix Tool within Esri's Network Analyst extension in conjunction with dasymetric mapping of US Census Data to the cadastral data. The demonstrated workflow provides for a highly detailed assessment of walking distance between parcels and parks, that when linked with the population data, provides a gap analysis based on the amount of parkland and number of parks available at each parcel. Successful completion of an analysis at this level of detail illustrates a very different view of park coverage for Wake County, NC compared to traditional methods, revealing how hard edges created by major thoroughfares and soft edges created by property ownership impact pedestrian accessibility. Using the cadastral-based method, 19.85% fewer parcels have 1/4-mile park access than compared to a buffer based method (6.72% versus 26.27%). The use of this type of technique will allow for a more comprehensive assessment of the peoples served by the park system and when coupled with demographic information, may prove more effective in assessing grants and monitoring the impact of public initiatives promoting equality and uniformity of access to public parks.

The U.S. Office of the Border Patrol defends the nation at its borders from unauthorized entry and terrorist incursion through the strategic application of detection, delay and response resources in variable terrain. Compounding their task, the expansive geography of the border region, along with a constrained budget, necessitate the allocation of resources to areas of greatest concern based upon a perceived threat that varies both spatially and temporally. The purpose of this research is to demonstrate a flexible geospatial decision support model that incorporates human and geographic variables identified through intelligence collection to define a threat and predict human route selection along a path of adversary least cost. Leveraging historical research into the characteristics and motivational factors of illegal border crossers, this research models a hypothetical terrorist threat to predict a route from a location near the U.S.-Mexico border to a predetermined location within the U.S. The model utilizes cost-weighted rasters representing postulated threat-based factors contributing to human route selection. The results of the model are intended to serve as a demonstration-of-concept to aid in defense resource allocation along the U.S.-Mexico border. It is anticipated that the results of this research will demonstrate a novel geospatial approach toward resource allocation through the synergy of intelligence information and spatial analysis techniques to yield likely transnational adversary routes.

This study demonstrates the utility of visualization-based spatiotemporal analysis as a policy support tool in the agricultural sector through a case study analyzing changes in the spatial distribution of tobacco farming in the Philippines from 1990 through 2012. Tobacco farming remains divisive in the Philippines; although often touted by tobacco companies and supportive government agencies as integral to the Philippine economy and an effective crop for poverty alleviation, recent studies dismiss these claims altogether, suggesting that farmers would be better off diversifying or even switching crops altogether (SEATCA 2008; Espino et al. 2009; World Health Organization 2012). This study does not argue for or against tobacco farming; it simply illustrates how spatiotemporal analysis can be successfully implemented to uncover deeper, more nuanced insights that could be drawn upon to design efficient and effective tobacco farming policies. The analysis considers provincial level agricultural data from the Philippines Bureau of Agricultural Statistics for tobacco area planted, volume of production and farm gate pricing of three unique tobacco varieties: Native, Virginia, and Burley. Stationary and dynamic techniques of spatiotemporal data visualization are used, and data are analyzed for trends using outlined methods. The results holistically describe tobacco farming in the Philippines and are drawn upon to determine which tobacco growing provinces and types of tobacco are on the rise or decline, to investigate causation behind spikes and dips in production, and to outline the future direction of the industry as a whole. The spatiotemporal analysis provides empirical evidence for policy makers to better understand regional and provincial trends in tobacco farming over time.

This project investigates gentrification-related displacement in the City of Los Angeles, California by introducing an analytic method that utilizes Volunteered Geographic Information (VGI). Data harvested from the social media network Twitter were analyzed and the results compared against an established method to assess risk of displacement that utilizes aggregated census data. Aggregated census data are problematic in displacement research due to spatial and temporal constraints. The purpose of this investigation is to advance research on displacement by introducing an alternative method to gain a better understanding of the dynamic nature of gentrification and displacement by leveraging spatially explicit real-time VGI data. This study examined approximately one million randomly harvested geotagged Twitter posts (tweets) within the City of Los Angeles, from August 2013 to January 2014, to investigate patterns of displacement. The research employed two frameworks: 1) a traditional census-based Data Aggregation Method; and 2) an alternative VGI (Twitter) based method. The results indicate that although tweets consisting of words related to displacement were not densely located in census tracts that have a high risk of displacement, as recorded by the Data Aggregated Method, areas of Los Angeles that are going through or just finished revitalization projects did contain such tweets. If left unmonitored, these areas could soon gentrify and displace as indicated by their demographic change over the last twelve years. In other words, the VGI Method detected a signal for potential displacement. Further, the VGI Method shows that data from Twitter produced results that are comparable to an established method of locating demographic change and go beyond an aggregated method's spatial and temporal level of analysis.

This research implements an additive travel cost model to calculate and compare the perceived cost of commuting by transit and driving at a disaggregated level. The model uses open source General Transit Feed Specification (GTFS) data and "Yay Transit!," an ArcGIS tool developed by Melinda Morang and Patrick Stevens of Esri, to create a transit network for the Washington DC metropolitan area. Departure sensitive route paths and travel times on transit are solved through the Route Tool of the ArcGIS Network Analyst Extension and compared to travel data calculated using Waze for driving between similar origins and destinations. Additional travel cost components are plugged into additive cost formulas designed to resemble the mode choice modeling formulas created by MWCOG (Metropolitan Washington Council of Governments) in order to compare the perceived cost of one mode over the other. Results from this model suggest that taking transit is in general less cost effective than driving for even some of the most transit advantageous commutes. Transportation Demand Management opportunities to most effectively "balance" the perceived cost of transit and driving are identified through assessing variable sensitivity of the additive formula. This research provides a methodology that could be reproduced in mass in order to gage the complex interconnectivity of an urban transportation network. The author suggests hosting this information in an online tool which will assist government and the public in understanding the cost effectiveness of transit versus driving for any given commute situation.

Geographic Information Systems (GIS) provide a good framework for solving classical problems in the earth sciences and engineering. This thesis describes the geostatistics associated with creating a geological model of a petroleum reservoir using a variogram-based two-point geostatistical approach. The geology of the field features a conventional heterogeneous sandstone formation with uniformly inclined rock strata of equal dip angle structurally trapped by surrounding geologic faults. Proprietary electrical well logs provide the resistivity and spontaneous potential at depth intervals of ten feet for the thirteen active wells in the field. The dimensions and shape of the reservoir are inferred from geological reports. An isopach map was georeferenced, digitized and used to generate a three-dimensional point-set grid illustrating the boundaries and the volumetric extent of the reservoir. Preliminary exploration of the input data using univariate and bivariate statistical tests and data transformation tools rendered the data to be statistically suitable for performing ordinary kriging and sequential gaussian simulation. The geological and statistical characteristics of the field indicate local stationarity thus ensuring that interpolation is appropriate to employ. Three variogram directions were established as part of the variogram parameters and then a best-fit statistical function was defined as the variogram model for each of the two electrical log datasets. The defined variogram was then used for the kriging algorithm. The data points were interpolated across the volumetric reservoir resulting in a 3D geological model displaying the local distribution of electrochemical properties in the subsurface of the field. Data is interchanged between separate modeling programs to illustrate the interoperability across different software. Validation of the predictive geostatistical models includes performing a leave-one-out cross-validation for each borehole as well as computing a stochastic model based on the sequential Gaussian simulation algorithm, which yielded multiple realizations that were used for statistical comparison. The reservoir characterization results provide a credible approximation of the general geological continuity of the reservoir and can be further used for reservoir engineering and geochemical applications.

Pesticide exposure estimation in epidemiologic studies can be constrained to analysis scales commonly available for cancer data - census tracts and ZIP codes. Research goals included (1) demonstrating the feasibility of modifying an existing geographic information system (GIS) pesticide exposure method using California Pesticide Use Reports (PURs) and land use surveys to incorporate Landsat remote sensing and to accommodate aggregated analysis scales, and (2) assessing the accuracy of two rurality metrics (quality of geographic area being rural), Rural-Urban Commuting Area (RUCA) codes and the U.S. Census Bureau urban-rural system, as surrogates for pesticide exposure when compared to the GIS gold standard. Segments, derived from 1985 Landsat NDVI images, were classified using a crop signature library (CSL) created from 1990 Landsat NDVI images via a sum of squared differences (SSD) measure. Organochlorine, organophosphate, and carbamate Kern County PUR applications (1974-1990) were matched to crop fields using a modified three-tier approach. Annual pesticide application rates (lb/ac), and sensitivity and specificity of each rurality metric were calculated. The CSL (75 land use classes) classified 19,752 segments [median SSD 0.06 NDVI]. Of the 148,671 PUR records included in the analysis, Landsat contributed 3,750 (2.5%) additional tier matches. ZIP Code Tabulation Area (ZCTA) rates ranged between 0 and 1.36 lb/ac and census tract rates between 0 and 1.57 lb/ac. Rurality was a mediocre pesticide exposure surrogate; higher rates were observed among urban areal units. ZCTA- level RUCA codes offered greater specificity (39.1-60%) and sensitivity (25-42.9%). The U.S. Census Bureau metric offered greater specificity (92.9-97.5%) at the census tract level; sensitivity was low (≤6%). The feasibility of incorporating Landsat into a modified three-tier GIS approach was demonstrated. Rurality accuracy is affected by rurality metric, areal aggregation, pesticide chemical class, and pesticide exposure cutoff. Future research should explore integrating Landsat for higher spatial resolution pesticide exposure estimation.

Spatial thinking is an abstract term and process in regards to what most of the general population understand. Many people are not well versed in geospatial terminology, options of use, and the location intelligence they unconsciously disclose when using social media outlets. This thesis integrates a unique technical web application with GIScience intended to illuminate the subsequent effect location-based data can have on one's personal privacy, security, and web-presence. An innovative new web mapping application was built for general public consumption that aggregates location data from Twitter, harvests ambient location information, analyzes the captured data to provide personal location intelligence, and visualizes possible areas of interest. In addition, this research examines the results of an online voluntary survey collected from the users of the application. Finally, this thesis discusses how these same techniques can be applied to other social media outlets along with potential opportunities to educate and inform the general public more about their social media location privacy.

The Electoral College (EC) has occasioned controversy at several points in its history, most recently in 2000 when George W. Bush was elected without winning the popular vote. One principal historical and contemporary argument in favor of the EC is that it performs a balancing function to lift the power of rural and less populous states. Using Geographic Information Systems (GIS) and the measure of voter power as formulated by Banzhaf (1968), this study puts this argument to an empirical test. It finds that the EC has not functioned to balance the electoral power of voters in urban and populous states with those in rural and less populous states throughout the 20th Century and into the 21st Century. Counterintuitively, by late in the 20th Century it actually enhances the electoral power of the largest and most heavily urbanized states. One partial exception to this finding is that the EC did significantly enhance the power of voters in the South in the decades before the Great Migration took place and civil rights legislation ensured equal voting rights. Analyses in this study uncover the voting rules within the EC that are behind these variations in voter power. The analyses and findings in this study leave a foundation for further study at the county scale that may aid in validating the results here.

Changing technologies and cultures make possible new ways of analyzing, understanding, and mapping religious geography. This study illustrates how GIS technology can provide a view of the details in the structures and adherents of the churchscape of America. GIS allows more detailed exploration of diversity in the American religious landscape than previous research has uncovered in spite of very limited data availability. This study has illustrated that the religious landscape of America is very complicated and multi-faceted. The physical locations tell us that our nation is a Baptist nation, whereas the adherent population tells us that our nation is a Catholic nation. The diversity of religious beliefs and practices that is part of the fabric of the country's foundation is also reflected in the current landscape. Cluster analysis of physical church locations shows us that churches cluster together regardless of denomination. This study raises questions regarding the exceptional nature of the American religious landscape. The findings call for other disciplines such as sociology, planning, and theology to examine in more detail the diversity found in the religious landscape of America.

The North Rainier Elk Herd (NREH) is one of ten designated herds in Washington State, all managed by the Washington Department of Fish and Wildlife (WDFW).To aid in the management of the herd, the WDFW has decided to implement a spatial ecosystem analysis. This thesis partially undertakesthis analysis through the use of a suite of software tools, the Westside Elk Nutrition and Habitat Use Models (WENHUM).This model analyzes four covariates that have a strong correlation to elk habitat selection: dietary digestible energy {ODE); distance to roads open to the public; mean slope; and distance to cover-forage edge and returns areas of likely elk habitation or use. This thesis includes an update of the base vegetation layer from 2006 data to 2011, a series of clear cuts were identified as areas of change and fed into the WENHUM models. The addition of these clear cuts created improvements in the higher quality DOE levels and when the updated data is compared to the original, predictions of elk use are higher. The presence of open or closed roads was simulated by creating an area of possible closures, selecting candidate roads within that area and then modeling them as either "all open" or "all closed". The simulation of the road closures produced increases in the higher levels of predicted use.

Using Volunteered Geographic Information (VGI) to model blue whale (Balaenoptera musculus) foraging habitat, this thesis assesses the utility of citizen science in cetacean research and marine spatial management. A unique and new data source on whale locations, observation data collected voluntarily by whale-watching vessels, was procured, compiled, and digitized. The utility of this newfound dataset was investigated through its use in probabilistic habitat suitability analyses and description of species phenology. A statistical analysis of whale observations was used to quantify seasonal variability of three common baleen whale species within the study area. Among these, blue whales exhibit the highest degree of seasonal variability with a mean seasonal abundance occurring in late July. Maximum entropy modeling was used to illustrate potential blue whale foraging areas based on three environmental variables: bathymetry, sea surface temperature, and chlorophyll-a concentrations. Spatial patterns of whale observations recorded by whale watchers and scientists indicate a strong habitat preference of steep bathymetric features in and around the 300-m isobath. Models using whale-presence data collected by whale-watchers were compared to similar models using science-quality whale observation data. Differences between these models are minimal and the results of the comparison support the usefulness of citizen science in cetacean research.

The Federal Emergency Management Agency (FEMA) has broad responsibility for both hazard mitigation and response throughout the United States. For natural hazards, FEMA in 2011 released a major update of its GIS-based predictive modeling tool, HAZUS-MH 2.0? (hereafter HAZUS), which deals with earthquakes, floods, and severe weather events. For the latter two perils, losses to agriculture are modeled along with losses to life and property. This study offers an assessment of the HAZUS crop flood loss modeling methodology for Fremont County Iowa, specifically for heavy flooding that occurred there in June-August 2011. Fremont County had the largest estimated financial losses due to crop damage amongst all Iowa counties from the 2011 flood. This assessment compares HAZUS model runs against actual crop losses as determined by both the National Agricultural Statistical Service (NASS) and by the Iowa Farm Bureau Federation (IFBF). Predicted agricultural losses were generated using both HAZUS' riverine method and the HAZUS user defined depth-grid methods. These results were compared against the actual NASS harvested acreage and yield results. The HAZUS results were also compared against a special IFBF study for Fremont County, which used the USDA IMPLAN? economic impact tool. Overall, differences among the HAZUS predictions and reality varied by up to 390%; differences between HAZUS and the IFBF predictions varied by up to 214%. FEMA's HAZUS consistently overestimated. Based on the Fremont County flood, improvements in the HAZUS crop loss methodology are urgently recommended.

The second generation of the Landscape Disturbance and Succession model (LANDIS?II) is frequently used to understand ecological succession on the landscape. LANDIS?II is an important simulation tool but it can be difficult to parameterize properly in data?poor regions. By examining the spatial sensitivity of LANDIS--?II, the model's users will have an improved understanding of the data required to properly implement the model. Existing studies have tested the ecological sensitivity of LANDIS?II in local geographic settings, but a robust test of the model's spatial sensitivity has not been completed. This research tested the spatial sensitivity of the LANDIS-II spatially stochastic landscape model using a broad set of vegetation communities found within the contiguous United States. Thirty spatially explicit, equalarea, and area-weighted iterations of the spatial parameters of the LANDIS-II model were run for a series of localities in the contiguous United States, where the areas were defined by the spatial composition of vegetation community values. Ecological attributes were derived from the Nature Serve Ecological Systems of the United States dataset. A test of the spatial input parameters of LANDIS-II demonstrated that the model is aspatial under certain conditions. Furthermore, vegetation community interactions may be effectively represented in LANDIS-II by a series of spatially stochastic input rasters; such that assessing a locality's vegetation trend is possible even when spatially explicit land classification information is unavailable, thereby facilitating long-term environmental planning in data-poor environments.

A "trade area" represents the geographical space from which a business draws customers and "market area" refers to the larger geographic region in which several businesses compete. Market areas are usually defined by an arbitrary spatial extent, such as a political boundary or radius around a given point, which injects arbitrariness into any market share calculations. In the staple goods retail sector, travel time and physical location are critical to consumer store preference. Therefore, the accurate delineation of a store's geographical trade area and surrounding market is needed for precise performance measurement and market share calculation. A framework is proposed for defining retail market areas based on degrees of spatial separation between a store and surrounding customers and competitors. Four degrees of customer separation are proposed, classifying customers based on their consideration of, and/or preference for, a specific store location. The result is a less arbitrary definition of a market. The proposed approach is modeled for the grocery sector of Worcester, Massachusetts. Resulting market share calculations are compared and the implications of using one method over another are discussed.

The threat of avalanches to winter recreationalists and mountain communities is well known. Geographic Information Systems (GIS) technology has been used to augment avalanche forecast and control programs in many parts of the United States and Europe. Successful GIS approaches combine terrain modeling, historical avalanche data and avalanche flow modeling to identify and predict avalanche probability and intensity for relatively small geographic areas (e.g., highway corridors, commercial ski areas and municipalities) (McCollister and Birkeland 2006). However, little research has focused on the vast backcountry areas between such small, populated areas. With the advent of lighter, better equipment for both backcountry skiers and snowmobilers, recreationalists increasingly visit these areas and are at risk from avalanches. Thus, an effort to reevaluate and improve avalanche risk information available to winter recreationalists is warranted. This study developed and evaluated geoprocessing methods using readily available spatial data to identify two terrain features of particular importance in evaluating avalanche risk (e.g., depositional terrain traps and trigger points) and create a forest density coverage for display and geoprocessing purposes. Field trials with produced results demonstrated that such methods could improve decision making and route finding in winter backcountry areas.

Little is known about the Nile crocodile (Crocodylus niloticus) population in Madagascar; however, its population is believed to be in decline resulting from hunting and habitat loss. This study maps the distribution of the Nile crocodile population in the Mariarano River in Northwestern Madagascar during the dry season (May-October) using the maximum entropy model Maxent. Four biophysical factors are included in the first model and the second model includes two additional anthropogenic factors of distance from roads and distance from villages to observe the effect of humans on suitable habitat for crocodiles. Data were collected in June- August 2011 and 2012. Model performance was assessed using the Receiving Operating Curve (ROC) and Area under the Curve (AUC), using 10 replicates of both models. Both models adequately predicted species occupancy using the test data: the anthropogenic model receiving model performance rating of excellent and the biophysical factor-only model receiving a rating of average. While the results initially indicated that the distance from roads was the most important variable to the model, other possible anthropogenic influences such as boat activity on the river and mangrove destruction were not included. The distribution map produced for the model can be used as a baseline for Nile crocodile distribution within the river and aid in conservation management decisions about the Nile crocodile in the region.

San Antonio in Bexar County is the seventh largest-populated city in the United States, and resides centrally in the state of Texas (United States Census 2010). Texas ranks first in total roadway miles by ownership, with over 300,000 roadway miles built for public use (United States Census 2012). With such a vast roadway infrastructure comes many critical problems including wrong-way driving (WWD), the focus of this study. An Environmental Systems Research Institute (Esri) ArcGIS geoprocessing task, Closest Facility, utilizing Network Analyst 10.1 extension has been customized to create a Wrong-Way Driving Transportation Model (WWD Model) (Esri 2013). This model directly addresses several key challenges faced by the San Antonio Wrong Way Driver Task Force (herein referred to as Task Force). Using geographic information systems (GIS) this model performs a route analysis that models the travel paths of such crash incidents from their likely point of origin - alcohol-serving facilities as determined by the Task Force (San Antonio Wrong Way Driver Task Force 2012). The WWD Model methodology is structured such that a specified Network Dataset - in this case, roadways provided by Bexar County Metro 911 - is analyzed to route WWD crash incidents from the nearest suspected facilities of origin. The customized geoprocessing toolkit then utilizes the resulting polyline dataset output to estimate the route taken by drivers based on the validated spatial relationship of reported crash incidents to reported WWD events as recorded in real-time by TransGuide Operators. A data validation of the resulting

Solar radiation is a promising source of renewable energy because it is abundant and the technologies to harvest it are quickly improving. An ongoing challenge is to find suitable and effective areas to implement solar energy technologies without causing ecological harm. In this regard, one type of land use that has been largely overlooked for siting solar technologies is closed or soon to be closed landfills. By utilizing Geographic Information System (GIS) based solar modeling, this study takes an inventory of solar generation potential for such sites in the State of California. The study takes account of various site characteristics in relation to the siting needs of photovoltaic (PV) geomembrane and dish-Stirling technologies (e.g., size, topography, closing date, solar insolation, presence of landfill gas recovery projects, and proximity to transmission grids and roads). This work reaches three principal conclusions. First, with an estimated annual solar electricity generation potential of3.7 million megawatt hours (MWh), closed or soon to be closed landfill sites could provide an amount of power significantly larger than California's current solar electric generation. Secondly, the possibility of combining PV geomembrane, dish- Stirling, and landfill gas (LFG) to energy technologies at particular sites deserves further investigation. Lastly, there are many necessary assumptions, challenges, and limitations when conducting inventory studies of solar potential for specific sites, including the difficulty in finding accurate data regarding the location and attributes of potential landfills to be analyzed in the study. Furthermore, solar modeling necessarily simplifies a complex phenomenon, namely incoming solar radiation. Lastly, site visits, while necessary for validating details of the site, are largely impractical for a large scale study.

Between 2008 and 2010, the number of unmanned aircraft systems (UAS) in the military increased by 30% in support of operations throughout the Middle East (Iraq, Afghanistan, Iran, etc.). The Pentagon has developed numerous initiatives to enhance the overall performance of UASs, demonstrating that reliance on and deployment of these systems is expected to continue. Via real-time aerial imagery, UASs provide commanders with continuous intelligence-gathering in hostile territories, without placing personnel in imminent danger; however the intelligence collected is valuable only if it is accessible. The data communications capabilities of UASs are severely restricted due to the limitations of bandwidth in the battlefield. Transmission of imagery, in raw form, consumes large amounts of bandwidth. Increasing transmission bandwidth is not a feasible solution in battlefield conditions. Reducing the size of transmissions, imagery in this case, is the only realistic approach. This thesis demonstrates the use of wavelet compression on UAS imagery to better support military combat operations, thereby reducing the "fog of war" and saving lives. Specifically this thesis studies ERDAS?' Enhanced Compression Wavelet (ECW) technology, which allows compression and decompression of imagery without placing a large burden on processors and memory (necessarily limited in UASs) and thereby economizing the use of data communications networks. Tests using simulated battlefield equipment show that image compression of 93%, and a concomitant decrease in bandwidth demands, is possible.

The interface between former wildland and urban sprawl is of major concern in the Western United States throughout wildfire-prone areas. Kern County, California, northwest of Los Angeles, is one such heavily impacted area. Recent major wildfires there have portrayed extreme fire behavior and caused significant property damage underscoring the need for fire prevention efforts before emergency response. This thesis demonstrates the utility of pre-incident planning (PIP) maps for wildfire mitigation built using geographic information system (GIS)-based cartography. PIP maps highlight imperative spatial information for emergency responders during the first, crucial "golden hour" of a wildfire, particularly accurate locations for structures and water sources, along with ratings of roadways for fire engine access. The PIP approach would not be possible without GIS, in fact, owing to the need for an accurate, up-to-date spatial data and voluminous map production. In both concept and execution, PIP maps, have proven valuable far beyond their original intention aiding in at least a dozen major wildfires since 2008, helping to protect over 4000 structures. In addition, PIP maps have shown qualitative benefits, improving firefighter safety, incident organization, and emergency communication. Constructing PIP maps for Kern County cost $115,000; the return on investment is estimated in the millions of dollars.

This thesis uses a geographic information system (GIS) to demonstrate spatial analysis techniques in order to examine changes to a prehistoric society of Native American Wappo dating from 2450 to 1950 years before present (BP) from the Upper Archaic Period in the Napa Valley of California. This cemetery was excavated by Pacific Legacy Inc., a private cultural resources management firm, in compliance with the National Historic Preservation Act (NHPA) and the California Environmental Quality Act (CEQA) for a flood control project. While Pacific Legacy Inc. analyzed the burials on an individual basis, they did not conduct a spatial analysis. They incorporated their data into a simple spreadsheet to look for patterns. This thesis serves as a complimentary spatial examination of the burials based on spatial data. The dataset is incomplete as it was not collected using a consistent, systematic methodology. Additional burials related to the dataset had also been removed from the site before excavation by erosion and other archaeological excavations. This paper demonstrates select spatial analysis techniques using this dataset as an example. This thesis examines the distribution of the burials within the cemetery to identify spatial patterns based on burial attributes and artifact distribution. Spatial autocorrelation, cluster analysis, and grouping analysis focus on identifying burial clusters and individual burial outliers. A form of interpolation known as kriging was used to estimate the dates for the burials that were not subjected to Accelerator Mass Spectrometry (AMS) Radiocarbon dating. The burials were then grouped into corresponding date ranges covering one hundred year time spans. This experimental study allows for identification of changes to society by analyzing the change in burial attributes and artifact types over the course of the Upper Archaic Period. Due to the incomplete nature of the dataset, only two conclusions could be reached with the remaining findings considered suggestive. There is clustering based on bone preservation and the spatial analysis results tend to vary depending on different excavation techniques. Possible clustering of depth, wealth diversity index, directly associated shell beads, and directly associated pendants may reflect certain aspects of ancient society. The possible clustering of artifact association, total tools, tool diversity index, indirectly associated bifaces, indirectly associated edgemodified flakes, indirectly associated unifaces, and indirectly associated pestles can likely be explained due to differing excavation techniques. Possible clustering of natural obsidian needles may be explained as naturally occurring in the soil. Dental caries were found to be possibly dispersed, which is likely just a random occurrence. The experimental radiocarbon date interpolation allowed for an examination of changes to CA-NAP- 399 over a five hundred year period. Thus results from the analyses in this report should not be seen as definitive nor should they be used as foundations for further archaeological analysis. The main purpose here is to demonstrate how spatial analysis may be used with data of this type.

Rainfall maps produced with data from widely dispersed official government weather stations are generalized maps covering broad geographical areas that provide little detail at larger scales. Little research has been completed in producing surfaces at smaller extents due to the lack of available data. A non-traditional method of obtaining additional data is through Volunteered geographic information (VGI), which presents data from non-authoritative sources that often supplement traditional data sources, and make analyses not previously considered, now possible. This thesis used citizen collected rainfall measurements, VGI, to create rainfall surface representations of a small geographic area located within the Kona Districts on the Big Island of Hawaii. The geostatistical methods of ordinary kriging, cokriging, and Empirical Bayesian Kriging (EBK) were used to interpolate these rainfall point location averages and create rainfall surface maps. Prediction error statistics were generated that corresponded to each surface representation and were used to determine the most accurate method. The resulting maps that were created for the study area were at least as good as those produced by traditional authoritative sources. An examination of a cluster of citizen rainfall gauges within a smaller sub-region of the study area was used to create rainfall maps with greater spatial variation compared to maps created from government stations. EBK provided the most accurate results nine out of twelve times, while using the least amount of input.

The distribution and management of water resources in the Western United States has become a critical issue. Limited and declining sources of water are regulated by legislation. The key regulatory principle is the prior appropriation doctrine that states the senior water rights holders are allowed to use water before any rights granted at a later date. Prior appropriation is significant during dry seasons or low water levels in streams. The regulation of such a system requires a water manager to research the current status and location of each water right and associate it to a parcel and address. Government agencies responsible for regulating water rights in western states have implemented digital mapping and geographic information systems to streamline this process. However, it is necessary to improve the accuracy and availability of the water rights information in digital form to implement an efficient system for compliance investigations during regulation seasons. This study demonstrates the methods utilized to develop an accurate geographic information system in the Umpqua Basin in Oregon to support the Watermaster responsible for regulation through prior appropriation requirements.

Native plants have been cultivated and utilized for thousands of years. From medicine and food to shelter and clothing, native plants have played an integral role in forming the indigenous religions, languages and cultures of Southern California. In recent years there has been a revival of indigenous culture with a focus on using native plants to teach about the languages and traditions of native people. The "Mapping Native Plants" application developed as part of this thesis work is a novel iPhone application that puts selflearning tools into the hands of the general public. Using the theory of Volunteered Geographic Information (VGI) this application will give the power of the map to the people and not just to scientists and other specialists. Users can geotag the location of native plants, learn the plants' native names, and read about how native plants have been used for thousands of years. Geotagging involves the user creating a geographic point in the map interface of the application that will represent the native plant of their choice. An enterprise geodatabase was created using Esri ArcGIS 10.1 for Server to store the native plant data and allow multiple users to create and edit their own geotags of native plants. The geographic data embedded in the application is a product of Esri ArcGIS Online's web map and feature services so that anyone can view the data and reference it in their own ArcMap projects. Results from usability, performance and laboratory testing show that the application is understandable, easy to use in the field, effectively developed to run at optimal speeds on the iPhone, and that all functions and tools work without error. The application is more than just a map that will show the location of native plants, it is a tool of self-education that will open a new perspective on the environment in the eyes of users and allow them to access a wealth of indigenous plant knowledge that has evolved and persisted for millennia.

This investigation and analysis of land use/tree cover was conducted to determine the impact of land-use policy developments in a major city. The City of Riverside was selected as a case study for this investigation because it had the necessary attributes: aerial photos of the study area over two decades, including two different yet comparable areas, one under a form of land use restriction with politically active citizenry interested in preserving their agricultural heritage. The research question is, can land use policy changes be analyzed for effectiveness by analyzing changes in land use and tree cover over time? Land use is defined herein as residential,farmland,or orchards. Aerial imagery covering a 50- year time span was collected and loaded into a GIS system for analysis. The GIS analysis included identification of land use types, imagery analysis of tree cover, and the correlation of the imagery analysis with land use policy using a feature analyst/computer-aided classification system. The research identified a significant reduction in tree cover due to the transition from orchards to residential land use. The results illustrate the land use and tree cover consequences of greenspace conservation policies adopted by the City of Riverside in 1979 and 1987. These results indicate that changes to land use and tree cover can be linked to policy developments in major southern California cities. The challenges in conducting this research included the acquisition of aerial imagery data sets, and analytical tool selection for measuring land use and tree cover which could be accurately associated with local, state and Federal policy development. Remaining questions include the correlation of census and property tax roles to the land use changes that have been identified.

The land surveying community has discovered the economic benefits of managing their survey data within a single system and view a geographic information system (GIS) as a possible method of doing so. However, the traditional coordinate-based design of a GIS does not contain the means to retain or employ the use of original measurements collected by land surveyors, a legacy that has resulted in skepticism among the surveying community. Thus, if a land surveyor desires to manage surveying data within a GIS environment, that GIS should be a measurement-based GIS (MBGIS). This research describes a MBGIS based upon the rules and relationships of measured points within the metes and bounds surveying environment of the state of Texas. Since Esri's parcel fabric data model contains several characteristics that indicate it might be considered a measurement-based system, it is explored as a possible method to manage and retain the measurement-based elements of metes and bounds surveying within a GIS environment. This study concludes that although the parcel fabric model has limitations when compared to an ideal MBGIS, it does have the capability to manage metes and bounds survey data if proper preparation and management techniques are applied.

Los Angeles County is home to devastating wildfires that bum hundreds of thousands of acres and destroy many homes every year. There are a variety of reasons why some homes bum and others do not. For example, homes located along a Wildland- Urban Interface (WUI) usually means that the home is in what the Los Angeles County Fire Department calls a "Very High Fire Hazard Severity Zone" (VHFHSZ). Homes can bum due to the defensible space of surrounding vegetation and the types of structural materials. Itis important to understand why certain homes bum and others remain unharmed. This thesis uses mobile mapping in GIS to capture different fire risk attributes of homes located in Los Angeles County's VHFHSZ. The purpose of this study is to determine which homes have the greatest risk of burning so that improved mitigation techniques can be implemented to prevent those homes from igniting during future wildfires. The spatial video data is archived for post-wildfire analyses to conclude if a burnt home was damaged due to its building materials and surrounding vegetation. Results of the analyses have shown clusters of fire hazardous homes and have determined individual homes with high fire risk attributes. Ultimately, this research provides the Los Angeles County Fire Department with timely relevant data to improve mitigation plans and conduct post-fire investigations if a wildfire bums in the studied areas.

To improve bus route planning and understand walkability's role in bus network design, this study offers a method of evaluating the walkability of bus stops and provides a case study for stops along two bus routes in Orange County, California. Having better walkability for bus routes may both promote physical activity and encourage bus ridership. Previous studies on bus route planning focus mostly on the passengers' travels on the bus and minimal attention is given to the bus riders' experiences before reaching the bus, after departing the bus, and during transfers between bus lines. This study shows the relevance of considering the origin, destination, and walking paths for pedestrians when approaching bus network design problems. The walkability of the southbound bus stops along Route 47 and Route 89, operated by the Orange County Transportation Authority (OCTA), were evaluated by calculating and combining the scores of four variables within each bus stop buffer. The four variables evaluated were: population density, street connectivity, steepness, and tree canopy. Results show that Route 47 has higher overall walkability than Route 89, which is in accordance with the hypothesis that a route that runs through grid neighborhoods (Route 47) would be more walkable than a route that runs through cul-de-sac neighborhoods (Route 89). Sensitivity analyses demonstrated that walkability scores may change when a stop is repositioned to a hypothetical location further away from an arterial street and within a neighborhood. Although walkability will never be the sole factor in designing bus routes, future modeling could weigh the importance of walkability as part of origin and destination modeling and use the scoring of walkability to guide adoption of the "flexible-route" bus lines. Future research should consider other methods of determining tree canopy scores and explore other methods of identifying pedestrian "catchment" area of the bus stops.

Spatial research has been plagued by the modifiable areal unit problem, or MAUP (Openshaw, 1979), for decades. The MAUP can be broken down into two categories, the scale or aggregation effect and the zoning or grouping effect. Recent advances in spatial science and technology have exacerbated the effects of the MAUP prevalent in many forms of research. In this paper, data was obtained depicting instances of foreclosures in Los Angeles County (also the study extent) in 2006-2008. This data was spatially joined to three sets of grids, or fishnets, covering Los Angeles County. The data was also spatially joined to three additional datasets: the individual parcels of Los Angeles County and two aggregations of these parcels. Five cluster analysis tools were used to analyze each of these seven total datasets. Each permutation involved the five tools, seven datasets, and multiple pre-selected distance thresholds to test for scale and zoning effects of the MAUP. There were a total of 137 successful iterations illustrating the aforementioned permutations. It was determined the MAUP is prevalent in this case study. Suggestions are made in determining future actions to combat the effects of the MAUP.

Eelgrass (Zostera marina) is an important benthic flowering plant used by many marine species as a nursery and food source; it also sequesters carbon, and the beds provide some protection for shorelines from coastal erosion by slowing water movement. In the past century, approximately 90% of eelgrass beds have been lost from natural and anthropogenic causes. Eelgrass was once a major component of the shorelines of Long Island Sound, USA, which has experienced many of these effects, including rain runoff carrying pesticide and fertilizer residues. Knowledge and analysis of the water quality parameters in Long Island Sound influencing eelgrass distribution will enhance restoration efforts in the future. A GIS model was created that estimates the habitat suitability for all areas in Long Island Sound with respect to key environmental variables. The habitat model has two parts. First, the study area was limited to regions where eelgrass growth is possible based solely on water depth, assuming that other conditions are suitable. Second, this suitable area was ranked by weighting each of 11 environmental parameters: percent light reaching bottom (0--30), sediment grain size (0--15), Chlorophyll a (0--10), Total Suspended Solids (0--10), Total Dissolved Nitrogen (0--5), Total Dissolved Phosphorous (0--5), surface temperature (0--10), saliuity (0--5), pH (0--5), dissolved oxygen (0--5), and sediment percent organics (0--5). The resulting sum indicates the suitability of areas with a weighted sum of 100 being most suitable and 0 being least suitable. The model produced weighted sum scores ranging from 43 to 93.5. Areas that are scored higher than 75 within the suitable band should be locally tested to decide if the area is ready for habitat restoration to proceed. Regions below this threshold should be further tested to identify which parameter scores reduced the overall score. This identification of the parameter contributing to the low score could help prioritize policies to reduce these influences in the future.

In an effort to explore smart growth principles, this study offers an empirical test of the influence of the built environment at the neighborhood scale on vehicle transit behavior. Using U.S. Census data combined with spatial analysis techniques, the study conducts a cross-sectional analysis of the effect of the built environment on household automobile ownership and vehicles miles traveled (VMTs) in 75 block groups across five metropolitan statistical areas. Variables are measured for density, job and retail access, transit accessibility, and street connectivity. The study also considers confounding variables including household income, regional density, extent of regional transit network, age of neighborhood population, and individual transit expenditure. From these data, best-fit regression models are developed for VMTs and automobile ownership. Although there is significant unexplained variation, the regression models confirm a statistically significant association of VMTs and automobile ownership with the built environment. Among the implications of these findings are that (1) neighborhood density should be encouraged in areas well-served by transit, (2) transit and smart-growth projects will have a greater impact on VMTs in regions that have robust, existing transit systems, and (3) new transit projects will likely be most effective in reducing vehicle ownership if planners focus on better serving moderate and low-income neighborhoods. Future research should examine statistical associations longitudinally, based on updated data from the 2010 U.S. Census, and should attempt to gather primary data on VMTs at the household and neighborhood scales.

Student out-of-school suspensions have been an ongoing problem in US schools for many years. Current methods of analysis have not yielded new insights into this problem. The purpose of this thesis is to consider student suspension incidents from a spatial perspective. Using student level data provided by SBCUSD, a large urban school district in southern California, suspension incidents were geocoded and mapped to student home neighborhoods within the district for the purpose of identifying whether or not suspensions incidents are clustered and, if so, to determine by neighborhood where the clusters are located. Spatial analysis indicated that suspension incident clustering does exist. Hotspot analysis showed variations in the suspension incident clustering pattern when disaggregating results by significant student subgroups and incident types. Neighborhoods were classified by these patterns and the results visualized in a choropleth map. As a final step in the analysis, a geographically weighted regression model predicting district wide suspension incidents by census block group was developed. The model, based on the total number of days previously suspended and the number of students identified as having a low socioeconomic status, had an adjusted R-squared greater than 0.90. Additional research needs to be conducted to verify that the patterns noted within this thesis hold steady. If so, discipline issues within SBCUSD may in part be influenced by local neighborhood factors. This becomes an opportunity for the school district to act at a local level and identify strategies to reduce suspensions and improve student outcomes.

Wind energy was the fastest growing form of renewable energy in the world during the last decade and forecasts predict that this trend will continue. In the U.S., Renewable Portfolio Standards (RPS) and federal tax incentives drive this trend from a policy perspective, but despite its potential to reduce C02 emissions and dependence on foreign fuel for electricity generation,wind energy development remains a contentiousissue and siting of wind power systems remains problematic.This thesis presents a GIS-based tool for preliminary site suitability analysis for Onshore Wind Power Systems (ONSWPS) that can be used to addressthese issues from a planning perspective.This tool incorporates MultiCriteria Analysis (MCA) and the Analytical Hierarchy Process (AHP) along with various forms of spatial and sensitivity analysis to provide quick visual access to ONSWPS site selection information through a series of suitability maps.

Waterfowl are one of our Nation's most precious and abundant natural resources, and preserving habitat well suited to their needs has long been a goal of private and public entities alike. Inthis study, I focused on the American Black Duck (Anas rubripes), a species seeing a large decline in numbers since the mid-20th century. Using a satellite telemetry dataset collected by Ducks Unlimited during 2008 and 2009 in the context of the Protected Areas Database of the United States (PAD), I addressed the land use habits of A. rubripes to assess the efficacy of costly conservation efforts implemented through conservation easements and the maintenance of wildlife refuges and management areas. Most analyses were conducted at the stopover level, grouping telemetry points within a 0.5 decimal degree diameter. By creating distributions and studying correlations, this study finds that during wintering months A. rubripes registered more telemetry points in PAD lands where hunting is allowed in-season; during migration, lands outside of the PAD were more frequently used. This could be attributed to waterfowl specific management practices creating prime habitat during wintering and food needs being fulfilled by residual agricultural products during migration. This suggests an increased importance of management efforts in wintering habitats. Climate variables were also assessed to test reported influences of temperature and precipitation on distribution and stopover behaviors, but study data did not demonstrate a correlation between stopover length and temperature or precipitation at arrival and departure. A finer scale geospatial analysis using more detailed information about hunting status and protection level is recommended to further interpret available data.

One of the major responsibilities of a city government is management of real property, both public and private, within its jurisdiction. Classically, land is described by parcel (an aerial geospatial feature) while structures are referenced by address (a pseudo-spatial text string). Handwritten, typed, and computerized address lists in spreadsheets and non-geospatial databases have been and continue to be used by the various departments within city governments. Inevitably, these lists are unevenly updated and inconsistent in various ways. Modern data management systems, specifically Microsoft Excel, contain tools for standardizing tabular data, including addresses. Geographic information systems (GIS), which can be used to manage parcel and address data directly, have traditionally relied upon street centerline or parcel geocoding to spatialize an address and determine its location. Utilizing Excel and geocoders together, to create a complete and reliable master address file (MAF), can help a city government operate more efficiently. Explicitly spatializing the relationship between addresses and parcels by converting textual addresses to address points (APs) in a GIS database, is critical for many aspects of city business operations, because doing so allows the points to be mapped. This thesis demonstrates that an accurate and complete set of APs is a superior solution to street centerline or parcel geocoding. APs can be created from a city government's multiple, internal spreadsheets and databases, utilizing Microsoft Excel and GIS in combination with street centerline and parcel geocoding, resulting in an MAF and APs that can be used citywide.

An analysis of urban morphology in the Los Angeles metropolitan area was conducted. Specifically, Local Indicators of Spatial Association (LISA) were used to identify clusters of different types of urban amenities. A centrality score was calculated for every location based on the number of spatially coincident clusters, which was used to delimit the central place. The methodology, which was validated in the Chicago and New York metropolitan areas, employed multiple regular hexagonal arrays into which amenity location points were aggregated. These arrays, whose results were combined for a final analysis output, mitigated against the Modifiable Areal Unit Problem (MAUP) and revealed urban structures operating at multiple scales. Prevailing methods for delimiting urban centrality tend to reduce urban place to a monetary space by focusing on employment centers, commuting patterns, or 'central' land uses in order to identify a downtown or a Central Business District (CBD). This study elevates the experience of place within urban structure to identify an ambiguously bounded and internally inconsistent central place: a postmodern urban center. The study reveals both polycentrism and a strong core center in Los Angeles. The core center, called the Wilshire/Santa Monica Corridor, is delimited in detail.

Salts and nutrients are common contaminants in urban groundwater systems, and at certain levels these pollutants have been associated with adverse effects on agriculture, corrosion and mineral deposits on industrial piping, a decrease in the drinkability of water, and serious health problems. Groundwater pollution can stem from both natural and anthropogenic sources and given the high costs of remediation, groundwater managers are tasked with monitoring groundwater contamination and controlling its sources. With its large population, close proximity to the coastline and arid climate, Los Angeles County provides an important study area for the spatial and temporal analysis of salt and nutrient constituents across each of its I0 groundwater basins. This thesis study utilizes the California Regional Water Quality Control Board data set consisting of groundwater quality samples drawn from underground storage tanks, site clean-up programs and land disposal sites to determine the spatiotemporal patterns across each basin. Results show that no spatiotemporal pattern was recognized, except that the salt constituents routinely exceeded the respective Basin Plan limits (unlike the nutrient constituents). In the end, more conclusive results could be determined with additional analysis and modeling that was better designed for sample collection and better controlled over the locations and depths at which the samples were taken.

Sustainable ranching refers to the practice of evaluating livestock quantities that natural grasses and ecosystems are capable of supporting, with minimal long-term impacts on the environment. Defining optimal and sustainable stocking rates can be a complex problem for land managers striving to implement the practice of sustainable ranching of sheep. I used a combination of Geographic Information Systems (GIS) with Remote Sensing (RS) to analyze environmental variables and track movement patterns of sheep and tested it at the Lava Lake Livestock and Landscape Ranch. A GIS model utilizing remotely sensed imagery was built to identify areas capable for grazing by sheep across the study area. Tracking Analyst and Time Slider, which are GIS based time analysis tools, utilized point data collected from Global Positioning System (GPS) collars to visualize the rate at which sheep are traveling. Results show an estimated 85% of the study area is found capable for grazing with the primary eliminating factors being steeper terrain in the north and lack of water in the south. Results also outline two contrasting sheep patterns: a slower travel rate in autumn within the northern regions; a faster travel rate during spring in the more southern regions of the study area. An improvement in achieving even distribution of grazing, offering more resources such as water, and planning rest breaks of intensely used areas can be incorporated in future management plans. A continuation of the project would benefit from a closer look at vegetation specifically plant species type in the various terrains and a biomass study as well as factors affecting vegetation such as precipitation.

Community gardens foster many potential benefits, including food security, environmental stability, neighborhood beautification, and community cohesion (Wakefield, Yeudall, Taron, Reynolds, & Skinner, 2007). Social capital, commonly recognized as the sense of community, is an intangible asset fostered through civic engagement and correlated to increased quality of life (Putnam, 1993). This model tested the viability of social capital as a measurable indicator for community garden planning in conjunction with traditional agricultural criteria modified for urban agriculture in Akron, Ohio. The study identified vacant parcels in areas with fewer hubs of civic engagement in which to place community gardens as a tool for fostering social capital. Inan adapted methodology, this study introduces spatial components to social capital at the neighborhood scale, drawing from the theory behind Putnam's work to measure community involvement through membership counts at individual hubs of civic engagement. Sites with greater need for social capital were identified. The principal hubs of civic engagement identified were churches, which appeared to be a limitation to the study. Further field work to identify site-specific social hubs will be required for this method of measurement to be applied in Akron and other cities. However, the basic methodology is an effective tool in site suitability analyses for community gardens and social capital.

The objective of this thesis is to design and perform an experimental study to demonstrate how census data, urban land-cover classifications, and dasymetric mapping may be combined together to map and measure urban growth. The experiment explored urban growth using 1990, 2000, and 2010 census population data and satellite-derived urban land-cover data. The premise of this thesis was that if the combination of these techniques is found to provide an effective method of measuring urban growth, then urban planners and city managers should be advised to use them together when measuring development patterns and forecasting growth scenarios of urban areas. Census tract population data for 1990, 2000, and 2010 and satellite- derived urban land-cover data were used for the analysis. The study areas included the Lower Rio Grande Valley, TX (LRGV) region and the Hidalgo County Metropolitan Planning Organization (MPO) planning area. Census tract relationships were established across census years to account for changes in tract boundaries. Population counts from 2000 and 2010 were adjusted to 1990 census tract boundaries. Population change, growth rate, and the share of regional growth were calculated for each census tract to identify areas that have experienced substantial growth. Results showed that four census tracts within the Hidalgo County MPO planning area contributed to nearly 25% of the growth of the entire region. Additionally, the Hidalgo County MPO planning area accounted for nearly 70% of the growth of the entire region. Landsat 5 Thematic Mapper (TM) imagery was acquired to coincide with each census year. Landsat images where classified using a 30-class ISO Cluster unsupervised classification. Results from these classifications where used to create training samples for high, medium, and low density urban land-covers. Supervised classification was performed for each year resulting in three urban land-cover classes and one uninhabited class. Classification results were explored and plotted for each year to determine land-cover changes by urban density type. Results showed that the Hidalgo County MPO planning area has seen an increase in medium density development and a decline in low density development in the past decade. Multi-class weighted dasymetric mapping was performed using the aforementioned census tract data and urban land-cover classifications. The Dasymetric Mapping Extension (DME) for ArcMap 10 was utilized. Dasymetric population density maps were compared to choropleth population density maps. Dasymetric results where explored further for the four census tracts that contributed most to the region's population growth. Results indicate that the majority of the population resided in medium density developments by 2010, however, the areas that contributed most to population growth were still composed largely of low density urban development.

This study demonstrates the integration of Geographic Information Systems (GIS) with asset management. There are few existing studies or demonstrations of the integration of GIS technology with asset management systems, especially for vertical assets at water utilities. A model is developed using Otay Water District (OWD) as a case study. The case study expands upon a GIS model that already contains horizontal assets (e.g., pipelines). The new model includes vertical assets (e.g., pump stations). In the past, non-spatial vertical assets, such as pump stations and their components were represented only by a point and could not be plotted against spatial data variables. In the expanded model, spatial and nonspatial asset risk variables are measured and scored for the 79 pumps within the 20 pump stations at the district. Each pump is assigned criticality and probability scores, which are then multiplied to give an overall risk factor score. Model scores were plotted on a point symbology map and expert confirmation was conducted with OWD water operations staff. A sensitivity analysis of the model reveals that manipulating model parameters to increase overall scoring accuracy of some pumps can also have a negative impact on the scoring of others. Further study is needed to plan and implement schemes that allow vertical assets at utilities to inherit asset management scores based on their positions within larger horizontal networks.

There is a finite amount of fresh water available for use by water users (geologic processes, plants, animals, and even humans). Thus, conflicts and disputes often arise over water allocation, especially in the western United States, where water reserves are already scarce. Water rights systems and policies are designed to allocate water fairly even when water runs short. However, the science and legal principles behind these water rights systems are difficult to communicate to stakeholders, leading to reduced participation and legitimacy of policies (Priscoli 2004; Reisner 1993). Earlier work suggests that interactive maps can support or enhance stakeholder knowledge creation or refinement by promoting exploration of map data (MacEachren 2000; MacEachren and Brewer 2004; Andrienko and Andrienko 1999). This study explores approaches to visualizing water rights policies at multiple scales in communities and landscapes of the Ruby River Basin in Montana. A series of interactive maps was created and shared with stakeholders to obtain feedback based on expert local knowledge. The results suggest that interactive maps are powerful vehicles for communicating water right policies to stakeholders if careful attention is paid to applying cartographic design principles in maps properly contextualized for local conditions. Results also suggest that interactive maps are particularly useful in multiple representations of data that cannot be conveyed effectively through symbology. Future research is needed to test whether such maps actually improve stakeholder knowledge and perception, and in tum spur public participation.

The 2008 presidential election and Sarah Palin's use of the term "real" America sparked a national debate about whether this concept further divided the nation into two distant parts, Red America and Blue America. For many the term Red America is meant to speak to "average"Americans who live in "average" places and earn "average" incomes. This is a major issue because the idea that ''real" American places are a common occurrence is incorrect. Using an extensive literature review and advanced GIS techniques this study uses general social data to isolate actual geographic areas based on normative archetypes from political discourse, areas referred to as ''real" and "fake" America. The study also challenged the notion of ''real" America by finding the most "average"American places, the areas that best reflect the nation as a whole, and produced an "average"American landscape. The final part of the study compared these outputs and deciphered whether an area's 'realness or averageness' has a connection to recent political voting trends. To be clear the point of the study is not to find a place to label the 'real America', the point is to use the search itself as a means to demonstrate a problem. The question is not does the ''real" America exist, there will be places that closely resemble the concept, the question is whether or not the ''real" America speaks to a sizable percentage of the US population, and whether or not it describes the living conditions of the 'average American'.

Inrecent years, concerns about fuel costs, environmental degradation and climate change have prompted consideration of alternative methods for electrical power generation. Studies have revealed that solar technology offers an environmentally sensible alternative to traditional electrical generation methods. However, in order for thistechnology to take effect, rules, regulations, and geospatial requirements must be met. Site selection becomes more problematic and the restrictions regarding land development can delay a project by months or even years. This study demonstrates how a geographic information system can be effectively used to spatially reconcile select prospect facility locations in a given region based on pre-existing geographic constraints. Prior literature, in conjunction with expert opinion, was used to define the appropriate search criteria. Area, slope, location, proximity to utilities, direct insolation, and critical habitat were just a few of the geographic criteria taken into consideration. By using Esri's Spatial Analyst and other data driven inquiries, regions of undesired terrain were omitted leaving only the available sites favored for CSP solar development on BLM lands within San Bernardino County, California.

Species distribution models use species occurrence data and environmental variables to estimate species-habitat relationships and predict potentially suitable habitat. This research analyzes the usefulness of a maximum entropy model, Maxent, for estimating species occurrences and environmental predictor variables for Hydromantes shastae, a rare species of salamander with a small geographic extent and limited occurrence records. Environmental variables included elevation, geology, land cover, precipitation, and soils. Seventy-five percent of the presence data was used to train the model and the remaining 25% was used for testing. Model performance was measured by area under the Receiver Operating Characteristics (ROC) curve (AUC). The AUC of 0.879 indicated that the model performed substantially better than a random prediction. The log loss plot indicated that soils contributed most to model fit. These results indicate Maxent's effectiveness for identifying potentially suitable habitat for H. shastae and predicting potential species occurrences. This model can be used to support species impact analyses and conservation efforts. Further, this model could be enhanced to focus surveys for populations in new areas and predict species responses to altered environmental conditions.