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                            Library & Multimedia
 A Statistical Approach to Crime Linkage
NCJ Number
              255192

Author(s)
          Michael D. Porter
Date Published

2016
Length
              14 pages
Annotation
              This article reports the development of a statistical approach to criminal linkage analysis that discovers and groups crime events that share a common offender and prioritizes suspects for further investigation.
Abstract
              Bayes factors are used to describe the strength of evidence that two crimes are linked. Using concepts from agglomerative hierarchical clustering, the Bayes factors for crime pairs are combined to provide similarity measures for comparing two crime series. This facilitates crime series clustering, crime series identification, and suspect prioritization. The ability of the proposed models to make correct linkages and predictions is demonstrated under a variety of real-world scenarios with a large number of solved and unsolved breaking-and-entering crimes; for example, a naive Bayes model for pairwise case linkage can identify 82 percent of actual linkages with a 5-percent false positive rate. For crime series identification, 74 percent-89 percent of the additional crimes in a crime series can be identified from a ranked list of 50 incidents. (publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2010-DE-BX-K255


  
  
        
  Agencies
        NIJ-Sponsored


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


  
  
        
  Award
        Statistical Methods for Spatio-Temporal Crime Series Linkage Analysis
    
  
  
Date Published: January 1, 2016
    
          Downloads
        
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