Book chapter
Exploring Spatial Patterns of Crime Using Non-hierarchical Cluster Analysis
Crime Modeling and Mapping Using Geospatial Technologies, pp 105-124
26 Nov 2012
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.
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28 citations in Scopus
Details
- Title
- Exploring Spatial Patterns of Crime Using Non-hierarchical Cluster Analysis
- Creators
- Alan T. Murray - Arizona State UniversityTony H. Grubesic - Drexel University
- Publication Details
- Crime Modeling and Mapping Using Geospatial Technologies, pp 105-124
- Series
- Geotechnologies and the Environment
- Publisher
- Springer Netherlands; Dordrecht
- Resource Type
- Book chapter
- Language
- English
- Scopus ID
- 2-s2.0-84935126996
- Other Identifier
- 991019357635304721