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Place-based correlates of Motor Vehicle Theft and Recovery: Measuring spatial influence across neighbourhood context

Author

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  • Eric Piza

    (John Jay College of Criminal Justice, USA)

  • Shun Feng

    (John Jay College of Criminal Justice, USA)

  • Leslie Kennedy

    (Rutgers University, USA)

  • Joel Caplan

    (Rutgers University, USA)

Abstract

Social scientists have long shown great interest in the spatial correlates of crime patterns. A subset of the literature has focused on how micro-level spatial factors influence the formation of crime hot spots. At the same time, tangential research has highlighted how neighbourhood disadvantage influences crime occurrence. The current study focuses on the intersection of these perspectives through a spatial analysis of Motor Vehicle Theft (MVT) and Motor Vehicle Recovery (MVR) in Colorado Springs, CO. We begin by conducting a Risk Terrain Modelling analysis to identify spatial risk factors significantly related to MVT and MVR occurrence. We then test whether the spatial influences of the criminogenic risk factors differ across traditional measures of neighbourhood disadvantage. Findings suggest that while a citywide effect is evident for multiple risk factors, their spatial influence on crime significantly varies across neighbourhood contexts.

Suggested Citation

  • Eric Piza & Shun Feng & Leslie Kennedy & Joel Caplan, 2017. "Place-based correlates of Motor Vehicle Theft and Recovery: Measuring spatial influence across neighbourhood context," Urban Studies, Urban Studies Journal Limited, vol. 54(13), pages 2998-3021, October.
    • Handle: RePEc:sae:urbstu:v:54:y:2017:i:13:p:2998-3021
    DOI: 10.1177/0042098016664299
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    References listed on IDEAS

    as
    1. Walsh, Jeffrey A. & Taylor, Ralph B., 2007. "Community structural predictors of spatially aggregated motor vehicle theft rates: Do they replicate?," Journal of Criminal Justice, Elsevier, vol. 35(3), pages 297-311.
    2. Basta, Luke A. & Richmond, Therese S. & Wiebe, Douglas J., 2010. "Neighborhoods, daily activities, and measuring health risks experienced in urban environments," Social Science & Medicine, Elsevier, vol. 71(11), pages 1943-1950, December.
    3. Drawve, Grant & Thomas, Shaun A. & Walker, Jeffery T., 2016. "Bringing the physical environment back into neighborhood research: The utility of RTM for developing an aggregate neighborhood risk of crime measure," Journal of Criminal Justice, Elsevier, vol. 44(C), pages 21-29.
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    1. Connealy, Nathan T. & Piza, Eric L., 2019. "Risk factor and high-risk place variations across different robbery targets in Denver, Colorado," Journal of Criminal Justice, Elsevier, vol. 60(C), pages 47-56.
    2. Thomas, Shaun A. & Drawve, Grant, 2018. "Examining interactive effects of characteristics of the social and physical environment on aggravated assault," Journal of Criminal Justice, Elsevier, vol. 57(C), pages 89-98.
    3. Seppo Virtanen & Mark Girolami, 2021. "Spatio‐temporal mixed membership models for criminal activity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1220-1244, October.

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