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

Matthew Quick ()
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Matthew Quick: Arizona State University

Journal of Geographical Systems, 2019, vol. 21, issue 3, No 2, 339-365

Abstract: 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.

Keywords: Spatiotemporal; Crime pattern; Multilevel model; Neighbourhood; Cross-classified data (search for similar items in EconPapers)
JEL-codes: C11 C33 R23 R58 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10109-019-00305-2

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