Applications for Fall 2024:
Opens on Monday, January 1, 2024.
The deadline to apply is Friday, June 7, 2024.
Applications are accepted for admissions in the fall and spring semesters; no summer semester admissions.
What’s unique about this program?
In the USC M.S. in Spatial Economics and Data Analysis (SEDA) program, 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.”
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.
Applicants must have:
- 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 must be admitted in an admissions process coordinated by both the USC Department of Economics and the USC Spatial Sciences Institute.
A STEM program
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.
- 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.
8 required courses (32 units total)
A minimum cumulative GPA of 3.0 is required to graduate.
Requirements for graduation, course offerings, course availability, track offerings and any other data science degree requirements are subject to change.
Students should consult with an academic advisor in the Spatial Sciences Institute prior to registering for any classes.
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.
Year 1, Semester 2
Application of econometric tools using standard econometric software packages for microcomputers; empirical applications to selected economic problems of estimation and inference.
Provides the knowledge and skills necessary to investigate the spatial patterns which result from social and physical processes operating at or near the Earth’s surface. 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.
Theoretical foundations, methods, techniques, and software systems for spatial econometrics, investigating the effects of spatial dependence and spatial heterogeneity.
Year 2, Semester 4
Economics Elective Courses: (Choose one course for 4 units)
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.
Year 2, Semester 4
Spatial Elective Courses: (Choose one course for 4 units)
Introduce the spatial data scientific approach to issues and present a holistic generalizable analysis pipeline.
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.
Talk with an enrollment advisor
To learn more if the USC M.S. Spatial Economics and Data Analysis program is right for you, please email email@example.com.