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NCJ Number: 229871 Find in a Library
Title: (Looking) Back to the Future: Using Space-Time Patterns to Better Predict the Location of Street Crime
Journal: International Journal of Police Science and Management  Volume:12  Issue:1  Dated:Spring 2010  Pages:23-40
Author(s): Lisa Tompson; Michael Townsley
Date Published: 2010
Page Count: 18
Type: Research (Applied/Empirical)
Format: Article
Language: English
Country: United Kingdom
Annotation: This article investigates the degree to which spatial patterns of crime are predictable and the degree to which improvements in forecasting can be achieved when temporal information is included in the analysis.
Abstract: Crime analysts attempt to identify regularities in police recorded crime data with a central view of disrupting the patterns found. One common method for doing so is hotspot mapping, focusing attention on spatial clustering as a route to crime reduction (Chainey & Ratcliffe, 2005; Clarke & Eck, 2003). Despite the widespread us of this analytical techniques, evaluation tools to assess its ability to accurately predict spatial patterns have only recently become available to practitioners (Chainey, Tompson, & Uhlig, 2008). Crucially, none has examined this issue from a spatio-temporal standpoint. Given that the organizational nature of policing agencies is shift based, it is common-sensical to understand crime problems at this temporal sensitivity, so there is an opportunity for resources to be deployed swiftly in a manner that optimizes prevention and detection. This paper tests whether hotspot forecasts can be enhanced when time-of-day information is incorporated into the analysis. Using street crime data, and employing an evaluative tool called the Predictive Accuracy Index (PAI), we found that the predictive accuracy can be enhanced for particular temporal shifts, and this is primarily influenced by the degree of spatial clustering present. Interestingly, when hotspots shrank (in comparison with the all-day hotspots), they became more concentrated, and subsequently more predictable. This is meaningful in practice; for if crime is more predictable during specific timeframes, then response resources can be used intelligently to reduce victimization. Tables, figures, notes, and references (Published Abstract)
Main Term(s): Crime patterns
Index Term(s): Crime prediction; Demographic analysis of crime; Geographic distribution of crime; Location specific crime; Police resource allocation; Street crimes; United Kingdom (UK)
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