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Risk factor and high-risk place variations across different robbery targets in Denver, Colorado

Author

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  • Connealy, Nathan T.
  • Piza, Eric L.

Abstract

Risk Terrain Modeling (RTM) has been effectively used to spatially diagnose risk for crimes such as robbery, aggravated assault, and gun violence. An important contribution is to consider how risk differs across individual crimes and different target types. This study tests four different robbery target types in unique models to examine the potential for variation across significant risk factors and high-risk locations.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jcjust:v:60:y:2019:i:c:p:47-56
    DOI: 10.1016/j.jcrimjus.2018.11.003
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    References listed on IDEAS

    as
    1. Piza, Eric L. & Gilchrist, Andrew M., 2018. "Measuring the effect heterogeneity of police enforcement actions across spatial contexts," Journal of Criminal Justice, Elsevier, vol. 54(C), pages 76-87.
    2. Valasik, Matthew, 2018. "Gang violence predictability: Using risk terrain modeling to study gang homicides and gang assaults in East Los Angeles," Journal of Criminal Justice, Elsevier, vol. 58(C), pages 10-21.
    3. 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.
    4. 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.
    5. Lersch, Kim Michelle, 2017. "Risky places: An analysis of carjackings in Detroit," Journal of Criminal Justice, Elsevier, vol. 52(C), pages 34-40.
    6. 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.
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    Cited by:

    1. Álvaro Briz‐Redón & Jorge Mateu & Francisco Montes, 2022. "Identifying crime generators and spatially overlapping high‐risk areas through a nonlinear model: A comparison between three cities of the Valencian region (Spain)," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(1), pages 97-120, February.

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