Exploring spatial patterns of crime using non-hierarchical cluster analysis

Alan T. Murray, Tony H. Grubesic

Research output: Chapter in Book/Report/Conference proceedingChapter

11 Scopus citations

Abstract

Exploratory spatial data analysis (ESDA) is a useful approach for detecting patterns of criminal activity. ESDA includes a number of quantitative techniques and statistical methods that are helpful for identifying significant clusters of crime, commonly referred to as hot spots. Perhaps the most popular hot spot detection methods, both in research and practice, are based on tests of spatial autocorrelation and kernel density. Non-hierarchical clustering methods, such as k-means, are less used in many contexts. There is a perception that these approaches are less definitive. This chapter reviews non-hierarchical cluster analysis for crime hot spot detection. We detail alternative non-hierarchical approaches for spatial clustering that can incorporate both event attributes and neighborhood characteristics (i.e., spatial lag) as a modeling parameter. Analysis of violent crime in the city of Lima, Ohio is presented to illustrate this for hot spot detection. We conclude with a discussion of practical considerations in identifying hot spots.

Original languageEnglish (US)
Title of host publicationCrime Modeling and Mapping Using Geospatial Technologies
PublisherSpringer Netherlands
Pages105-124
Number of pages20
ISBN (Electronic)9789400749979
ISBN (Print)9789400749962
DOIs
StatePublished - Jan 1 2013

Keywords

  • Clustering
  • Hot spots
  • Spatial patterns

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Engineering(all)

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    Murray, A. T., & Grubesic, T. H. (2013). Exploring spatial patterns of crime using non-hierarchical cluster analysis. In Crime Modeling and Mapping Using Geospatial Technologies (pp. 105-124). Springer Netherlands. https://doi.org/10.1007/978-94-007-4997-9_5