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                            Library & Multimedia
 Modeling the Social and Spatial Proximity of Crime: Domestic and Sexual Violence Across Neighborhoods
NCJ Number
              308324
Journal

*Journal of Quantitative Criminology* Volume: 37 Dated: 2021 Pages: 481-516
Author(s)
          Claire Kelling;
                        Corina Graif;
                        Gizem Korkmaz;
                        Murali Haran
Date Published

2021
Length
              36 pages
Annotation
              The authors’ goal for the research study reported here was to understand the social dynamics affecting domestic and sexual violence in urban areas by investigating the role of connections between communities, also known as area nodes.
Abstract
              In this paper, the authors develop techniques to incorporate two types of proximity, geographic and commuting proximity, in spatial generalized linear mixed models (SGLMM) in order to estimate domestic and sexual violence in Detroit, Michigan, and Arlington County, Virginia. Analyses are based on three types of CAR models (the Besag, York, and Mollié (BYM), Leroux, and the sparse SGLMM models) and two types of SAR models (the spatial lag and spatial error models) to examine how results vary with different model assumptions. The authors used data from local and federal sources such as the Police Data Initiative and American Community Survey, and used innovative methods adapted from spatial statistics to investigate the importance of social proximity measured based on connectedness pathways between area nodes. In doing so, they sought to extend the standard treatment in the neighborhoods and crime literature of areas like census blocks as independent analytical units or as interdependent primarily due to geographic proximity. Analyses showed that incorporating information on commuting ties, a non-spatially bounded form of social proximity, to spatial models contributed to better deviance information criteria scores (a metric which explicitly accounted for model fit and complexity) in Arlington for sexual and domestic crime as well as overall crime. In Detroit, the fit was improved only for overall crime. The distinctions in model fit were less pronounced when using cross-validated mean absolute error as a comparison criterion. Overall, the results indicate variations across crime type, urban contexts, and modeling approaches. Nonetheless, in important contexts, commuting ties among neighborhoods are observed to greatly improve our understanding of urban crime. If such ties contribute to the transfer of norms, social support, resources, and behaviors between places, they may, then, also transfer the effects of crime prevention efforts. (Published Abstracts Provided)
      
  
          Additional Details
Grant Number(s)
        2020-R2-CX-0033


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Advances in Spatial Statistical Methodology for Urban Crime
    
  
  
Date Published: January 1, 2021
    
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