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Prospective Hot-Spotting: The Future of Crime Mapping?

Open access
Bowers, K. J.·2004·British Journal of Criminology

Abstract

Existing methods of predicting and mapping the future locations of crime are intrinsically retrospective. This paper explores the development of a mapping procedure that seeks to produce ‘prospective’ hot-spot maps. Recent research conducted by the authors demonstrates that the risk of burglary is communicable, with properties within 400 metres of a burgled household being at a significantly elevated risk of victimization for up to two months after an initial event. We discuss how, using this knowledge, recorded crime data can be analysed to generate an ever-changing prospective risk surface. One of the central elements of this paper examines the issue of how such a risk surface could be evaluated to determine its effectiveness and utility in comparison to existing methods. New methods of map evaluation are proposed, such as the production of search efficiency rates and area-to-perimeter ratios; standardized metrics that can be derived for maps produced using different techniques, thereby allowing meaningful comparisons to be made and techniques contrasted. The results suggest that the predictive mapping technique proposed here has considerable advantages over more traditional methods and might prove particularly useful in the shift-by-shift deployment of police personnel.

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Metadata

Authors
Bowers, K. J.
Venue
British Journal of Criminology
Published
2004-05-07
Volume
44
Issue
5
Pages
641-658
ISSN
  • 0007-0955 (ISSN-L)
  • 1464-3529
OpenAlex
W1987685062
Cited by
315
References
0
FWCI
26.59
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Prospective Hot-Spotting: The Future of Crime Mapping? https://app.cochat.ai/papers/10.1093/bjc/azh036
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Bowers, K. J. (2004). Prospective Hot-Spotting: The Future of Crime Mapping? British Journal of Criminology. https://doi.org/10.1093/bjc/azh036

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.