Spatial Filtering in a Regression Framework: Examples Using Data on Urban Crime, Regional Inequality, and Government Expenditures
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.
KeywordsSpatial Autocorrelation Spatial Dependence Government Expenditure Spatial Effect Spatial Association
I would like to thank Giuseppi Arbia for his suggestions on an earlier version of this chapter. Serge Rey for suggesting the data used in the government expenditures example, and the editors, Luc Anselin and Raymond Florax, for helpful comments.
- Anselin L (1988) Spatial econometrics: methods and models. Kluwer, DordrechtGoogle Scholar
- Cliff AD, Ord JK (1973) Spatial autocorrelation. Pion, LondonGoogle Scholar
- Gujarati D (1992) Essentials of econometrics. McGraw-Hill, New YorkGoogle Scholar
- Pindyck R, Rubinfeld D (1981) Econometric models and economic forecasts. McGraw-Hill, New YorkGoogle Scholar