MS in Spatial Economics and Data Analysis
The M.S. in Spatial Economics and Data Analysis (SEDA) provides the best of two worlds: a curriculum of a master's in Economics with a core MS in Data Science curriculum, linked with the power of spatial sciences. In this innovative program, work with globally recognized faculty from the Department of Economics and the Spatial Sciences Institute in the USC Dornsife College of Letters, Arts and Sciences.
Applications for Fall 2023 will open on Monday, January 2, 2023.
The deadline to apply for Fall 2023 is Friday, June 9, 2023.
What is Spatial Economics?
The power of data analytics using location-based Big Data with insights from geospatial intelligence and urban economics to identify business opportunities and solve real-world problems.
What Makes This Program Unique From a Masters in Economics or Masters in Data Science?
Students engage with a rigorous quantitative curriculum that innovatively combines economic, data science, and spatial science principles and applies them to current societal challenges. Projects are oriented for students to synthesize and analyze spatial data to gain insights on applications of compelling interest to them.
As spatial Big Data grows in availability and significance, so too are the market opportunities growing for professionals who can convert this kind of real-time spatial and economic analysis into recommendations in the areas of their choice.
Read this article "What Can Be Learned from Spatial Economics?" by Stef Proost and Jacques-Francoise Thisse, Journal of Economic Literature 2019, 57(3), 575-643, and this World Bank report on "Spatial Finance: Challenges and Opportunities in a Changing World."
Here are some industry examples:
The Challenge: Determine and compare the differences in the growth rate of housing prices in various regions of a country.
The Spatial Economics Solution:
- Identify the association and magnitude of land characteristics and housing price variation by regions using geographically weighted regression.
- Based on spatial regression analysis, which identified the actual housing price growth is affected variably by urbanization rate, average income, and proportion of renters in different cities and regions, housing policies should be regulated by local and regional agencies in order to maintain the health of the residential real estate market in the country.
The Challenge: Increase the competitiveness of local food producers by integrating their networks of suppliers, distributors, customers and community.
The Spatial Economics Solution:
- Evaluate the integrated food distribution network performance using spatial and network analysis.
- Compare the supply chain performance and logistic efficiencies in various scenarios, with and without integrated distribution networks.
- Identify the best existing and potential locations of distribution center(s) that can improve the number of routes (64%), numbers of visits (53%), transport distance (74%), transport time (63%).
- The improved supply chain performance will not only save logistic cost and time, but also improve food and service quality and reduce environmental impact (vehicle emission).
The Challenge: Determine economic incentives that reduce extreme traffic congestion at certain locations at peak demand times.
The Spatial Economics Solution:
- Using geo-coded road sensors, assemble and mine large-scale geo-coded data sets.
- Analyze the data through maps and other visualizations to identify the spatial patterns in traffic congestion by city, day of the week, and time of day.
- Design and implement field experiments to test whether a given congestion charge (such as charging drivers $6 from 4 pm to 7 pm) would be effective at reducing traffic congestion.
- With actionable information, recommend policies that would improve traffic flow.
The global geospatial industry is expected to grow at a 13.6% compound annual growth rate between 2017-2020 to reach US $440 billion in 2020.
Professionals with masters in economics and spatial data analysis education can bring new insights into emerging business opportunities, environmental trends, urban crime, congestion trends, global security, real estate valuation, technology diffusion, and countless other commercial and social applications around the world.
Through internships, guest speakers, industry meet-ups, and other career development opportunities such as the annual Geospatial Summit, students will build a professional network in Big Data industries and startups.
The USC M.S. in Spatial Economics and Data Analysis has the U.S. Department of Education CIP code of 45.0702 for GIScience and Cartography, identified as a STEM CIP code.
Meet Some of Our Faculty
8 required courses (32 units total)Year 1:Semester 1:Theories of the household and the firm; product and factor markets; perfect and imperfect competition; welfare criteria.The unique characteristics and importance of spatial information as they relate to the evolving science, technology, and applications of Geographic Information Systems.Semester 2:Application of econometric tools using standard econometric software packages for microcomputers; empirical applications to selected economic problems of estimation and inference.Design, implementation, and interrogation of relational, object-oriented and other types of geospatial databases. Recommended preparation: SSCI 581.Year 2:
Semester 3:Provides an introduction to the theory and practice of causal econometrics in modern settings of large-scale data (proposed for AY '21-'22).Theoretical foundations, methods, techniques, and software systems for spatial econometrics, investigating the effects of spatial dependence and spatial heterogeneity.Semester 4:
Choice of One ECON Elective:Learn to design, analyze and interpret field experiments and understand their practical significance to applied economics, business and policy.Economic methods to analyze issues of intellectual property, environmental damage, trademark infringement, brand value and consumer demand, using an applied econometric approach. Pre-Requisite: ECON 513.The role of designing incentives to reduce negative urban externalities and the interplay between spatial Big Data and testing urban economics hypotheses.
Recommended Preparation: Statistics or Econometrics.
Choice of One SSCI Elective:Recommended Preparation: SSCI 581.Design, implementation, and interrogation of relational, object-oriented and other types of geospatial databases. Recommended Preparation: SSCI 581.Principles of visual perception, spatial cognition and cartographic design and their contributions to the maps, animations, virtual reality and multimedia displays produced with modern GIS.
- develop an in-depth understanding of the fundamentals of spatial economics;
- learn and apply spatial analysis and spatial modeling approaches to identify new business opportunities and new policy solutions addressing urban problems;
- gain valuable research experience in analyzing spatial "Big Data"; and
- develop professional development insights in this nascent field of spatial analysis.
- a baccalaureate degree from a regionally accredited college or university in the United States, or the equivalent of a baccalaureate degree in another country;
- a minimum cumulative undergraduate GPA of 3.0
Students can be admitted into the program with various backgrounds. However, students are encouraged to have completed:
- an introductory course in GIS and/or Remote Sensing (or proof of practical/field experience) and
- undergraduate courses in Principles of Economics, Macroeconomics, and Microeconomics.
Spatial Economics and Data Analysis Program Application Materials:
- All applicants must complete and submit the USC Online Application.
- Official Transcripts: Official transcripts from all colleges and universities attended, sealed by the institution. Mail transcripts to: USC Graduate Office of Admission University Park Campus Los Angeles, CA 90089-0915. If sending via a private carrier (e.g., UPS, FedEx, or DHL), send transcripts to: University of Southern California Office of Admission & Financial Aid 3601 South Flower Street Tyler 1 Los Angeles, CA 90089-0915. For more information regarding transcripts visit USC Graduate Admissions Office - Transcript Requirements.
- GRE General Test: Official and current GRE scores required. Satisfactory scores must be less than five years old. Official scores must be reported from ETS directly to USC using ETS school code 4852. A department code is not required.
- Letters of Recommendation: Minimum of 2 letters of recommendation, should be from faculty or others (supervisors, professional colleagues, etc.) qualified to evaluate your potential for graduate study. They should be on the letterhead of the letter writer and signed by the letter writer, submitted through the online graduate application.
- Statement of Purpose: The statement of purpose should describe succinctly your reasons for applying to the proposed data science program, your preparation for this field of study, study interests, future career plans, and other aspects of your background and interests which may aid the admissions committee in evaluating your aptitude and motivation for graduate study.
Additional Application Materials for International Applicants
English Language Proficiency: In addition to the general admission criteria listed above, international students whose first language is not English are required to take the TOEFL or IELTS examination to be considered a candidate for admission. USC graduate applicants are therefore expected to demonstrate their proficiency in English as part of the application process. Please review the video tutorial titled, “English Proficiency Requirements for International Students” for more details. For possible exemption from additional language requirements, you must achieve an Internet Based TOEFL (iBT) score of 90, with no less than 20 on each section or an IELTS score of 6.5, with no less than 6 on each band score. For more details on English Proficiency Criteria for the University of Southern California, please visit USC Graduate Admission - Proficiency in English.
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