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

  • Arthur GetisEmail author
Part of the Advances in Spatial Science book series (ADVSPATIAL)


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.


Spatial Autocorrelation Spatial Dependence Government Expenditure Spatial Effect Spatial Association 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.


  1. Anselin L (1988) Spatial econometrics: methods and models. Kluwer, DordrechtGoogle Scholar
  2. Atalik G (1990) Some effects of regional differentiation on integration in the European community. Pap Reg Sci Assoc 69:11–19CrossRefGoogle Scholar
  3. Cliff AD, Ord JK (1973) Spatial autocorrelation. Pion, LondonGoogle Scholar
  4. Getis A (1990) Screening for spatial dependence in regression analysis. Pap Reg Sci Assoc 69: 69–81CrossRefGoogle Scholar
  5. Getis A (1993a) Introduction: mathematical models in geography. Pap Reg Sci 72:201–202CrossRefGoogle Scholar
  6. Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24:189–206CrossRefGoogle Scholar
  7. Gujarati D (1992) Essentials of econometrics. McGraw-Hill, New YorkGoogle Scholar
  8. Pindyck R, Rubinfeld D (1981) Econometric models and economic forecasts. McGraw-Hill, New YorkGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of GeographySan Diego State UniversitySan DiegoUSA

Personalised recommendations