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

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.

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Notes

  1. 1.

    Although crime ‘forecasting’ and ‘prediction’ are technically different concepts, they are often used synonymously in practice (see RAND, 2013). Therefore, we use the two terms interchangeably throughout this paper.

  2. 2.

    See a reference map of Brooklyn, NY on Google Maps: https://goo.gl/maps/aE7no4KH3SR2

  3. 3.

    This definition is consistent with New York State Penal Law, see CJI2d [NY] Penal Law §160.00. The NYPD classifies crime incidents using these definitions (see http://www.nyc.gov/html/nypd/html/crime_prevention/crime_statistics.shtml).

  4. 4.

    Robbery and risk factor data provided to the research team by the NYPD were likely geocoded using a composite method that matched first to streets then to parcels (which would explain the points that were offset as much as 160 ft). Since XY coordinates were provided to us, we do not know more about the exact geocoding method used by NYPD. For the point risk factors we manually geocoded, we used a 15-ft offset. Polygon risk factors, conversely, pertained to parcel and building footprints in New York city and were not georeferenced according to street centerline.

  5. 5.

    The robbery data used for this study was offset from street centerlines by as much as 160 ft. Most cells in the fishnet of Brooklyn were within this distance from streets. However, some other study settings may not be similar. For future research in other settings, we recommend that the fishnet used for testing predictive validity of KDE and/or RTM be limited to only those cells that intersect streets if the crime incident data used for the analysis are geocoded to street centerlines. Excluding non-intersecting cells before testing predictive validity would account for where crimes could actually occur within the study setting; It would exclude the cells that could never have a crime occur due to the technicalities of geocoding addresses to streets. This would likely enhance the results of future research.

  6. 6.

    We use ArcGIS to perform each KDE, which by default employs interpolation based on the quartic method.

  7. 7.

    We use the Nearest Neighbor Threshold (NNT) to select operationalization parameters. The NNT can be calculated using the following formula: 2 * (Block Length * Number of Analysis Increments). This formula produced a NNT of 4344. If the features were not significantly clustered or if the observed mean distance (reported by the NN analysis) were greater than the NNT, the ‘proximity’ to features was tested. If the features were significantly clustered and the observed mean distance was less than or equal to the NNT, ‘both’ proximity to and density of features was tested.

  8. 8.

    The two means of the average PAI values for RTM and KDE across all months were compared in t-tests with sample sizes of 11 months. The small size of sample (n = 11) could be a limitation to the statistical conclusion.

  9. 9.

    Risk terrain models produced for this study identified the most problematic environmental features for each month and for each quarter over the course of 1 year. It is interesting to note that while some of the 27 environmental features tested in the models were rarely or never identified as risk factors, others were consistently found to increase the risk of robbery. In particular, grocery stores were risk factors for robbery every month and food pantries and soup kitchens and subway entrances were risk factors in most months. Conversely, banks, billiard halls, chemical dependency facilities, cinemas, clubs, colleges and universities, homeless facilities, hospitals, hotels and motels, mental health facilities, developments, parking facilities, parks, postal facilities, and recreation centers were never risk factors for robbery.

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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.

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Correspondence to Joel M. Caplan.

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Caplan, J.M., Kennedy, L.W., Piza, E.L. et al. Using Vulnerability and Exposure to Improve Robbery Prediction and Target Area Selection. Appl. Spatial Analysis 13, 113–136 (2020). https://doi.org/10.1007/s12061-019-09294-7

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Keywords

  • Risk terrain modeling
  • Kernel density estimation
  • Prediction accuracy index