Spatial Filtering in a Regression Framework: Examples Using Data on Urban Crime, Regional Inequality, and Government Expenditures

Abstract

In a recent paper [Getis (1990)], I develop a rationale for filtering spatially dependent variables into spatially independent variables and demonstrate a technique for changing one to the other. In that paper, the transformation is a multi-step procedure based on Ripley’s second order statistic (1981). In this chapter, I will briefly review the argument for the filtering procedure and propose a simplified method based on a spatial statistic developed by Getis and Ord (1992). The chapter is divided into four parts: 1) a short discussion of the rationale for filtering spatially dependent variables into spatially independent variables, 2) a review of a Getis-Ord statistic, 3) an outline of the filtering procedure, and 4) three examples taken from the literature on urban crime, regional inequality, and government expenditures.

If autocorrelation is found, we suggest that it be corrected by appropriately transforming the model so that in the transformed model there is no autocorrelation [Gujarati (1992, p. 373)].