An area-based nonparametric spatial point pattern test: The test, its applications, and the future

Abstract

The analysis of spatial point patterns is a critical component of the geographic information analysis literature. Most of the tests for these data are concerned with random, uniform, and clustered patterns. However, knowing whether a spatial point pattern is similar to these theoretical data-generating processes is not always instructive: most human activity is clustered, so finding that some component of human activity is clustered is not really new information. In this article, a recently developed spatial point pattern test is discussed that compares the similarity of two different data sets. This comparison can be comparisons of different phenomena (different types of crime or public health issues) or the same phenomenon over time, for example. The discussion revolves around the test itself, its varied applications, and the future developments expected for this spatial point pattern test.