ABSTRACT
For researchers analyzing geographically oriented data, such as neighborhood or county crime rates, a serious threat to validity is the violation of OLS regression assumptions due to the spatial clustering of values for the variables in the analysis. In such circumstances, OLS regression estimates may be biased and inefficient. However, analytic methods for spatial data analysis allow formal assessments of spatial autocorrelation, as well as maximum likelihood spatial regression models. This paper presents discussions of the underlying logic of spatial effects, the impact of spatial dependence on non-spatial regression models, methods for assessing spatial dependence, the implementation of spatial regression models, and an illustration of the techniques presented. Additional discussion and references to more advanced spatial analytic techniques are provided.
