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Using Vulnerability and Exposure to Improve Robbery Prediction and Target Area Selection

NCJ Number
Applied Spatial Analysis and Policy Volume: Online Dated: March 2019
Date Published
March 2019
24 pages
This study examined the ways in which spatial vulnerabilities and exposures could be used to identify target areas for policing to deter and prevent robberies.
A large body of research has found that crime is much more likely to occur at certain places relative to others. Attempting to capitalize on this finding to maximize crime prevention, many police administrators have sought to narrow their department's operational focus and allocate resources and attention to the most problematic locations; however, in the face of a growing number of technological advances in crime forecasting that have facilitated this trend, it is still unclear how to best identify the most appropriate set of places to which resources and attention should be directed. The current study used the Theory of Risky Places as a guide, and employed kernel density estimation (KDE) to measure crime exposures and risk terrain modeling (RTM) to identify crime vulnerabilities, with the expectation that crime would be predicted more accurately by integrating the outputs from these two methods. To test this hypothesis, the analysis used 1 year of data on street robbery in Brooklyn, New York. A common metric, the prediction accuracy index (PAI), was computed for KDE, RTM, and the integrated approach, over 1- month and 3-month intervals. The study found that the integrated approach, on average and most frequently, produced the most accurate predictions. These results demonstrate that place-based policing and related policies can be improved via actionable intelligence produced from multiple crime analysis tools that are designed to measure unique aspects of the spatial dynamics of crime. 1 figure and 81 references (Publisher abstract modified)

Date Published: March 1, 2019