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Keywords:

  • Clustering;
  • Crime series;
  • Kernel density estimation;
  • K-function;
  • Monte Carlo tests;
  • Point patterns

Summary

Using 31 unique crime series from two US cities, the spatial pattern that individual criminal decision makers follow is investigated. Locations within a city vary in the likelihood for crimes of a given type to occur, and this is accounted for by using kernel density estimation based on all crimes of each type. Kernel density estimation is highly influenced by the bandwidth, so an objective approach is used to select the estimate. Then, the kernel density estimate for each type of crime is incorporated in an inhomogeneous K-function that can identify significant spatial clustering and/or uniformity at various spatial scales for each crime series. We find that robbery series are more likely to exhibit uniformity than burglary series, which tend to show strong clustering. In addition, the order in which new crimes are added to a series is found to follow an interesting pattern that does not always support the theory of offender as forager in which criminals first cluster crimes and then gradually disperse them. Half of the robbery series exhibit a spatial distribution that begins dispersed and develops clusters as increasingly more events are added to the series, indicating that they often return to locations of earlier crimes after having committed crimes elsewhere.