ABSTRACT
Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial nonstationarity. This chapter reviews previous approaches to mapping the results of GWR and suggests methods to improve upon them. It focuses on GWR as applied to the analysis of areal data, as opposed to data taken as samples of a continuous surface, as the vast majority of GWR research has been applied to socioeconomic data aggregated to census or other spatial units. As a case study, a number of mapping approaches are used to interpret the results of a GWR analysis of median home value in Philadelphia, Pennsylvania, USA using 2000 US Bureau of the Census tract level data. A software package devoted to automated mapping of GWR results would be a useful tool for assisting researchers in developing informative and useful maps for exploring spatial nonstationarity.
