Courses offered by the Committee on Geographical Sciences.

Information about Luc Anselin's Introduction to Spatial Data Science and Spatial Regression taught at the University of Chicago:

Introduction to Spatial Data Science (SOCI 20253/30253)

Spatial data science consists of a collection of concepts and methods drawn from both statistics and computer science that deal with accessing, manipulating, visualizing, exploring and reasoning about geographical data. The course introduces the types of spatial data relevant in social science inquiry and reviews a range of methods to explore these data. Topics covered include formal spatial data structures, geovisualization and visual analytics, rate smoothing, spatial autocorrelation, cluster detection and spatial data mining. An important aspect of the course is to learn and apply open source software tools, including R and GeoDa.

Autumn 2016 

Autumn 2017

Course description is the same as Autumn 2016.

Spring 2017
Spatial Regression Analysis (SOCI 40217)

This course covers statistical and econometric methods specifically geared to the problems of spatial dependence and spatial heterogeneity in cross-sectional data.  The main objective of the course is to gain insight into the scope of spatial regression methods, to be able to apply them in an empirical setting, and to properly interpret the results of spatial regression analysis.  While the focus is on spatial aspects, the types of methods covered have general validity in statistical practice.  The course covers the specification of spatial regression models in order to incorporate spatial dependence and spatial heterogeneity, as well as different estimation methods and specification tests to detect the presence of spatial autocorrelation and spatial heterogeneity.  Special attention is paid to the application to spatial models of generic statistical paradigms, such as Maximum Likelihood, Generalized Methods of Moments and the Bayesian perspective.  An important aspect of the course is the application of open source software tools such as R, GeoDa and PySal to solve empirical problems.