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                            Library & Multimedia
 Partially Supervised Spatiotemporal Clustering for Burglary Crime Series Identification
NCJ Number
              248840
Journal

*Journal of Royal Statistical Society: Series A (Statistics in Society)* Volume: 178 Issue: 2 Dated: February 2015 Pages: 465-480
Author(s)
          Brian J. Reich;
                        Michael D. Porter
Date Published

February 2015
Length
              16 pages
Annotation
              This article proposes a Bayesian model-based clustering approach for criminal events.
Abstract
              Statistical clustering of criminal events can be used by crime analysts to create lists of potential suspects for an unsolved crime to identify groups of crimes that may have been committed by the same individuals or group of individuals, for offender profiling and for predicting future events.  The authors approach is semi-supervised, because the offender is known for a subset of the events, and utilizes spatio-temporal crime locations as well as crime features describing the offender's modus operandi. The hierarchical model naturally handles complex features that are often seen in crime data, including missing data, interval-censored event times and a mix of discrete and continuous variables. In addition, the proposed Bayesian model produces posterior clustering probabilities which allow analysts to act on model output only as warranted. This approach is illustrated by using a large data set of burglaries in 2009-2010 in Baltimore County, Maryland.  (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)


  
  
        
  Award
        Statistical Methods for Spatio-Temporal Crime Series Linkage Analysis
    
  
  
Date Published: February 1, 2015
    
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                Burglary
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