Evaluation of spatial cluster detection algorithms for crime locations

Abstract

This comparative analysis examines the suitability of commonly applied local cluster detection algorithms. The spatial distribution of an observed spatial crime pattern for Houston, TX, for August 2005 is examined by three different cluster detection methods, including the Geographical Analysis Machine, the Besag and Newell statistic, and Kulldorff's spatial scan statistic. The results suggest that the size and locations of the detected clusters are sensitive to the chosen parameters of each method. Results also vary among the methods. We thus recommend to apply multiple different cluster detection methods to the same data and to look for commonalities between the results. Most confidence will then be given to those spatial clusters that are common to as many methods as possible. © 2012 Springer-Verlag Berlin Heidelberg.