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

  • Joel M. CaplanEmail author
  • Leslie W. Kennedy
  • Eric L. Piza
  • Jeremy D. Barnum
Article

Abstract

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. Our goal was to examine this issue by exploring the ways in which spatial vulnerabilities and exposures could be used to identify the best target areas for policing. Using the Theory of Risky Places as a guide, we 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, our analysis utilized 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. We found that the integrated approach, on average and most frequently, produces 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.

Keywords

Risk terrain modeling Kernel density estimation Prediction accuracy index 

Notes

Funding

This research was supported in part by a grant from the National Institute of Justice (Award #2013-IJ-CX-0053). The views presented are those of the authors and do not necessarily represent the position of the National Institute of Justice.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Criminal JusticeRutgers UniversityNewarkUSA
  2. 2.John Jay College of Criminal JusticeCity University of New YorkNew YorkUSA

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