Spatial Regression Analysis, Introduction to

Instructor(s): Paul Voss, Rural Sociology; Katherine White, University of Wisconsin at Madison

The goal of this five-day course is to provide an overview of applied spatial regression analysis (spatial econometrics) that will enable participants to effectively incorporate these tools into their own empirical research. This course will introduce the broader field of spatial data analysis and the range of issues that generally must be dealt with when analyzing georeferenced data. Census-type data are among the most commonly encountered data that conform to this description, although the course acknowledges the wider range appropriate for spatial regression analysis. The role of spatial autocorrelation in spatial data sets is a central focus. This course will address the following questions: how does spatial autocorrelation arise; how is it measured and understood; how does it relate to issues of spatial heterogeneity and spatial dependence; and how should it inform the specification and estimation of regression models. The course is structured around a combined lecture format (mornings) and computing lab exercises (afternoons). Although we will use mapping software, the focus of the course is on spatial analysis, not Geographic Information Systems (GIS). Software emphasis will be given to ArcGIS 9 and GeoDa for exploratory spatial data analysis (ESDA) and modeling. Some acquaintance with this software is helpful but is not a prerequisite. Prerequisites for maximizing learning in this course are a solid grounding in standard multivariate regression techniques and a minimal level of comfort with matrix notation and algebra.