M.S. in Spatial Economics and Data Analysis

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USC's M.S. in Spatial Economics and Data Analysis (SEDA) provides the best of two worlds: curriculum of a masters 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.

   Apply here   

The deadline to apply for Spring 2022 is November 5, 2021.

Applications for Fall 2022 open on January 3, 2022. The deadline to apply for Fall 2022 is June 10, 2022.

What is Spatial Economics?

The power of data analytics using location-based Big Data with insights from geographic information science and urban economics to identify business opportunities and solve real-world problems.

USC MS Spatial Economics and Data Analysis

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, in 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.

Career Opportunities:

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 in emerging business opportunities, environmental trends, urban crime, congestion trends, 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 Spatial Sciences Institute 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:

An-Min Wu
Elisabeth Sedano
Ergin Bayrak
Geert Ridder
Jennifer Bernstein
Laura Loyola
Matthew Kahn
Michael Sproul
Orhun Aydin
Paulina Oliva


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.
Understanding and implementing models commonly used in time series econometrics. Emphasis is placed on intuition and application. Assists students understanding how to use time series data to test hypotheses and serve as an introduction to the ideas and techniques of forecasting. Pre-Requisite: ECON 513.
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.

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.

Learning Objectives:

Graduates will:

  • 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.

Meet a Master of Science in Spatial Economics & Data Analysis Student:

Application Requirements:

  • 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;
  • official and valid TOEFL/IELTS/PTE scores if English language proficiency is listed under the country-specific requirements as maintained by the USC Graduate Programs Admissions office (check with dcaro@usc for COVID-19 accommodations);
  • a statement of purpose and CV/resume; and
  • a minimum of two letters of recommendation (students can submit as many as four).

Applicants may submit any additional documentation (e.g., posters, papers, or published articles), but these are not required.

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.

      Apply here     

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