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The Spatial Overlap of Police Calls Reporting Street-Level and Behind-Closed-Doors Crime: A Bayesian Modeling Approach

Author

Listed:
  • Miriam Marco

    (Department of Social Psychology, University of Valencia, 46010 Valencia, Spain)

  • Enrique Gracia

    (Department of Social Psychology, University of Valencia, 46010 Valencia, Spain)

  • Antonio López-Quílez

    (Department of Statistics and Operational Research, University of Valencia, Burjassot, 46100 Valencia, Spain)

  • Marisol Lila

    (Department of Social Psychology, University of Valencia, 46010 Valencia, Spain)

Abstract

Traditionally, intimate-partner violence has been considered a special type of crime that occurs behind closed doors , with different characteristics from street-level crime. The aim of this study is to analyze the spatial overlap of police calls reporting street-level and behind-closed-doors crime. We analyzed geocoded police calls in the 552 census-block groups of the city of Valencia, Spain, related to street-level crime ( N = 26,624) and to intimate-partner violence against women ( N = 11,673). A Bayesian joint model was run to analyze the spatial overlap. In addition, two Bayesian hierarchical models controlled for different neighborhood characteristics to analyze the relative risks. Results showed that 66.5% of the total between-area variation in risk of reporting street-level crime was captured by a shared spatial component, while for reporting IPVAW the shared component was 91.1%. The log relative risks showed a correlation of 0.53, with 73.6% of the census-block groups having either low or high values in both outcomes, and 26.4% of the areas with mismatched risks. Maps of the shared component and the relative risks are shown to detect spatial differences. These results suggest that although there are some spatial differences between police calls reporting street-level and behind-closed-doors crime, there is also a shared distribution that should be considered to inform better-targeted police interventions.

Suggested Citation

  • Miriam Marco & Enrique Gracia & Antonio López-Quílez & Marisol Lila, 2021. "The Spatial Overlap of Police Calls Reporting Street-Level and Behind-Closed-Doors Crime: A Bayesian Modeling Approach," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5426-:d:557839
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    References listed on IDEAS

    as
    1. Haining, Robert & Law, Jane & Griffith, Daniel, 2009. "Modelling small area counts in the presence of overdispersion and spatial autocorrelation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2923-2937, June.
    2. Enrique Gracia & Antonio López-Quílez & Miriam Marco & Marisol Lila, 2018. "Neighborhood characteristics and violence behind closed doors: The spatial overlap of child maltreatment and intimate partner violence," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-13, June.
    3. Leonhard Knorr‐Held & Nicola G. Best, 2001. "A shared component model for detecting joint and selective clustering of two diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 73-85.
    4. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    5. Quick, Matthew & Li, Guangquan & Brunton-Smith, Ian, 2018. "Crime-general and crime-specific spatial patterns: A multivariate spatial analysis of four crime types at the small-area scale," Journal of Criminal Justice, Elsevier, vol. 58(C), pages 22-32.
    6. Jane Law & Matthew Quick, 2013. "Exploring links between juvenile offenders and social disorganization at a large map scale: a Bayesian spatial modeling approach," Journal of Geographical Systems, Springer, vol. 15(1), pages 89-113, January.
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