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                            Library & Multimedia
 Using Vulnerability and Exposure to Improve Robbery Prediction and Target Area Selection
NCJ Number
              252726
Journal

*Applied Spatial Analysis and Policy* Volume: Online Dated: March 2019
Author(s)
          Joel M. Caplan;
                        Leslie W. Kennedy;
                        Eric L. Piza;
                        Jeremy D. Barnum
Date Published

March 2019
Length
              24 pages
Annotation
              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.
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. 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)
      
  
          Additional Details
Grant Number(s)
        2013-IJ-CX-0053


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
                                Report (Study/Research)
                                Report (Grant Sponsored)
                                Program/Project Description


  
  
        
  Award
        Policing by Place: A proposed multi-level analysis of the effectiveness of Risk Terrain Modeling for allocating police resources
    
  
  
Date Published: March 1, 2019
    
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