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Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach

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  • Matthew Quick

    (Arizona State University)

Abstract

Characteristics of the urban environment influence where and when crime events occur; however, past studies often analyse cross-sectional data for one spatial scale and do not account for the processes and place-based policies that influence crime across multiple scales. This research applies a Bayesian cross-classified multilevel modelling approach to examine the spatiotemporal patterning of violent crime at the small-area, neighbourhood, electoral ward, and police patrol zone scales. Violent crime is measured at the small-area scale (lower-level units) and small areas are nested in neighbourhoods, electoral wards, and patrol zones (higher-level units). The cross-classified multilevel model accommodates multiple higher-level units that are non-hierarchical and have overlapping geographical boundaries. Results show that violent crime is positively associated with population size, residential instability, the central business district, and commercial, government-institutional, and recreational land uses within small areas and negatively associated with civic engagement within electoral wards. Combined, the three higher-level units explain approximately fifteen per cent of the total spatiotemporal variation of violent crime. Neighbourhoods are the most important source of variation among the higher-level units. This study advances understanding of the multiscale processes influencing spatiotemporal crime patterns and provides area-specific information within the geographical frameworks used by policymakers in urban planning, local government, and law enforcement.

Suggested Citation

  • Matthew Quick, 2019. "Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach," Journal of Geographical Systems, Springer, vol. 21(3), pages 339-365, September.
    • Handle: RePEc:kap:jgeosy:v:21:y:2019:i:3:d:10.1007_s10109-019-00305-2
    DOI: 10.1007/s10109-019-00305-2
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    Cited by:

    1. Pina-Sánchez, Jose & Buil-Gil, David & brunton-smith, ian & Cernat, Alexandru, 2021. "The impact of measurement error in models using police recorded crime rates," SocArXiv ydf4b, Center for Open Science.
    2. Spencer, M. Dylan & Schnell, Cory, 2022. "Reinvestigating the relationship between cities and the spatial distribution of robbery: A tale of eight cities," Journal of Criminal Justice, Elsevier, vol. 82(C).
    3. Ugo Santosuosso & Alessio Papini, 2022. "An analysis about the accuracy of geographic profiling in relation to the number of observations and the buffer zone," Journal of Geographical Systems, Springer, vol. 24(4), pages 641-656, October.
    4. Daqian Liu & Wei Song & Chunliang Xiu & Jun Xu, 2021. "Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach," Sustainability, MDPI, vol. 13(19), pages 1-15, September.

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    More about this item

    Keywords

    Spatiotemporal; Crime pattern; Multilevel model; Neighbourhood; Cross-classified data;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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