ABSTRACT
This chapter we begin to explore the analysis of spatial autocorrelation statistics. It has been long known that attribute values that are close together in space are unlikely to be independent. This is referred to by Tobler's first law of geography: “everything is related to everything else, but near things are more related than distant things”. Spatial autocorrelation is the measure of this correlation between near things. In spatial statistics we generally distinguish global clustering from the detection of local clusters. Some techniques, the focus of this chapter, are based on the global view and are appropriate for assessing the existence of dependence in the whole distribution, or clustering. We introduce methods to assess spatial clustering with point pattern data, which typically start from exploring continuous distance between the location of the points. We discuss measures of global spatial autocorrelation for lattice data, which essentially aim to answer the degree to which areas (census tracts, police precincts, etc) that are near each other tend to be more alike.
