3 credits
Spring 2026 Lecture
This course deals with the analysis of spatial data and centers on both exploratory tools developed in spatial statistics and GIScience, as well as on econometric models that have been the main focus in spatial econometrics. During the course, the theoretical basis for the analysis of spatial data and spatial models will be covered. This theoretical angle will be combined with ample opportunities to acquire hands-on experience in the analysis of spatial data. To the effect, up-to-date software, such as GeoDa, R, Stata, and Matlab will be used. A good working knowledge of basic statistics and regression techniques is needed. Prior experience with GIS is helpful but not required.
Learning Outcomes1Explain fundamental concepts in spatial econometrics and network analysis.
2Conduct exploratory spatial and network data analyses (e.g., community detection, graph embedding, spatial autocorrelation).
3Select and justify appropriate spatial econometric models, including spatial lag, spatial error, and spatial Durbin specifications.
4Estimate and interpret spatial regression models using statistical software such as R, Stata, and MATLAB.
5Apply and interpret diagnostic tests for spatial dependence and model adequacy.
6Assess causal claims in empirical spatial studies and distinguish between spatial correlation and causal spillovers.
7Evaluate empirical research critically using spatial or network data.
8Design and complete an applied empirical project using real-world spatial or network datasets.
Prerequisites
Restrictions
made by Purdue students.