Crime-general and crime-specific spatial patterns: A multivariate spatial analysis of four crime types at the small-area scale. (September 2018)

• Record Type: Journal Article
• Authors: Quick, Matthew; Li, Guangquan; Brunton-Smith, Ian
• Is Part Of: Journal of criminal justice. Number 58(2018)
• Page Start: 22
• Page End: 32
• Publication Date: 2018-09
• Subjects: Spatial pattern -- Crime-general -- Correlation -- Multivariate -- Bayesian model -- Shared component; Criminal justice, Administration of -- Periodicals; Justice pénale -- Administration -- Périodiques; 364.05
• DOI: 10.1016/j.jcrimjus.2018.06.003
• Languages: English
• ISSNs: 0047-2352

Abstract

Abstract: Purpose: To examine if, and how, spatial crime patterns are explained by one or more underlying crime-general patterns. Methods: A set of Bayesian multivariate spatial models are applied to analyze burglary, robbery, vehicle crime, and violent crime at the small-area scale. The residual variability of each crime type is partitioned into shared and type-specific components after controlling for the effects of population density, deprivation, residential instability, and ethnic heterogeneity. Shared components account for the correlations between crime types and identify the crime-general patterns shared amongst multiple crimes. Results: Two shared components are estimated to capture the crime-general pattern for all four crime types and the crime-general pattern for theft-related crimes (burglary, robbery, and vehicle crime). Robbery and violent crime exhibit the strongest positive associations with deprivation, instability, and ethnic heterogeneity. Shared components explain the largest proportions of variability for all crime types. Burglary, robbery, and vehicle crime each exhibit type-specific patterns that diverge from the crime-general patterns. Conclusions: Crime-general patterns are important for understanding the spatial patterning of many crime types at the small-area scale. Multivariate spatial models provide a framework to directly quantify the correlation structures between crimes and reveal the underlying crime-general patterns shared amongst multiple crime types. Highlights: Multivariate spatial models are used to identify crime-general spatial patterns. Burglary, robbery, vehicle crime, and violent crime share a crime-general pattern. Burglary, robbery, and vehicle crime shsare a second distinct crime-general pattern. Crime-general patterns explain the largest proportions of variability for all crime types. Violent crime and robbery are positively associated with social disorganization variables.