Using Exploratory Spatial Data Analysis Techniques to Better Understand Housing Discrimination

This paper explores the potential for mapping with Geographic Information System (GIS) technology and Exploratory Spatial Data Analysis techniques using several different software packages to contribute to an understanding of housing discrimination in the City of Philadelphia. The primary research question is whether spatial statistical analysis offers insight beyond that provided by visual analysis of point patterns and area data. Various K-functions are used to test for significant clustering and spatial dependence between events while spatial autoregression and spatial lag programs are used to test for and model spatial autocorrelation. This varied approach leads to important conclusions concerning methodology as well as results relating to the spatial relationships among the location of different types of housing discrimination and to neighborhood characteristics including race and income.