Advanced Spatial Data Analysis Techniques in the Social Sciences

This course provides a survey of advanced spatial data analytical techniques with particular focus on methods relevant to applications in the social sciences. Topics include spatial process models, Bayesian analysis of spatial data, spatial models for discrete dependent variables (spatial counts, spatial probit and tobit, spatial multinomial models), spatial panel data (time series of cross-sections with spatial effects). The course introduces use of specialized software packages included in R and the PySAL library for spatial analysis in Python. SOC 2960G or equivalent is a prerequisite. The course requires a solid background in multivariate statistics, basic spatial statistics and spatial regression analysis.