Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm

Xiaolan Wu, Tony H. Grubesic

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Spatial cluster detection techniques are widely used in criminology, geography, epidemiology, and other fields. In particular, spatial scan statistics are popular and efficient techniques for detecting areas of elevated crime or disease events. The majority of spatial scan approaches attempt to delineate geographic zones by evaluating the significance of clusters using likelihood ratio statistics tested with the Poisson distribution. While this can be effective, many scan statistics give preference to circular clusters, diminishing their ability to identify elongated and/or irregular shaped clusters. Although adjusting the shape of the scan window can mitigate some of these problems, both the significance of irregular clusters and their spatial structure must be accounted for in a meaningful way. This paper utilizes a multiobjective evolutionary algorithm to find clusters with maximum significance while quantitatively tracking their geographic structure. Crime data for the city of Cincinnati are utilized to demonstrate the advantages of the new approach and highlight its benefits versus more traditional scan statistics.

Original languageEnglish (US)
Pages (from-to)409-433
Number of pages25
JournalJournal of Geographical Systems
Volume12
Issue number4
DOIs
StatePublished - Dec 1 2010

Keywords

  • Crime
  • Epidemiology
  • Genetic algorithms
  • Geographic information systems (GIS)
  • Hot-spots
  • Irregular clusters
  • Spatial analysis

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes

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