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                            Library & Multimedia
 Self-exciting Point Processes With Spatial Covariates: Modelling the Dynamics of Crime
NCJ Number
              254015
Journal

*Journal of the Royal Statistical Society Series C-Applied Statistics* Volume: 67 Issue: 5 Dated: 2018 Pages: 1305-1329
Author(s)
          Alex Reinhart;
                        Joel Greenhouse
Date Published

2018
Length
              25 pages
Annotation
              This study demonstrated that spatial and temporal factors related to crime patterns are generally confounded, requiring analyses to take both into account, and it proposes a spatio-temporal self-exciting point process model that incorporates spatial features, near repeat and retaliation effects, and triggering.
Abstract
              Crime has both varying patterns in space - related to features of the environment, economy and policing - and patterns in time arising from criminal behavior, such as retaliation. Serious crimes may also be preceded by minor crimes of disorder. The current study developed inference methods and diagnostic tools, such as residual maps, for the proposed model; and through extensive simulation and crime data obtained from Pittsburgh, Pennsylvania, demonstrated its properties and usefulness. (publisher abstracts modified)
      
  
          Additional Details
Grant Number(s)
        2016-R2-CX-0021


  
  
        
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        NIJ-Sponsored


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


  
  
        
  Award
        Point process modeling with spatiotemporal covariates for predicting crime
    
  
  
Date Published: January 1, 2018
    
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