TY - JOUR
T1 - Spatiotemporal Modeling of Correlated Small-Area Outcomes
T2 - Analyzing the shared and type-specific patterns of crime and disorder
AU - Quick, Matthew
AU - Li, Guangquan
AU - Law, Jane
PY - 2019/4/1
Y1 - 2019/4/1
N2 - This research applies a Bayesian multivariate modeling approach to analyze the spatiotemporal patterns of physical disorder, social disorder, property crime, and violent crime at the small-area scale. Despite crime and disorder exhibiting similar spatiotemporal patterns, as hypothesized by broken windows and collective efficacy theories, past studies often analyze a single outcome and overlook the correlation structures between multiple crime and disorder types. Accounting for five covariates, the best-fitting model partitions the residual risk of each crime and disorder type into one spatial shared component, one temporal shared component, and type-specific spatial, temporal, and space–time components. The shared components capture the underlying spatial pattern and time trend common to all types of crime and disorder. Results show that population size, residential mobility, and the central business district are positively associated with all outcomes. The spatial shared component is found to explain the largest proportion of residual variability for all types of crime and disorder. Spatiotemporal hotspots of crime and disorder are examined to contextualize broken windows theory. Applications of multivariate spatiotemporal modeling with shared components to ecological crime theories and crime prevention policy are discussed.
AB - This research applies a Bayesian multivariate modeling approach to analyze the spatiotemporal patterns of physical disorder, social disorder, property crime, and violent crime at the small-area scale. Despite crime and disorder exhibiting similar spatiotemporal patterns, as hypothesized by broken windows and collective efficacy theories, past studies often analyze a single outcome and overlook the correlation structures between multiple crime and disorder types. Accounting for five covariates, the best-fitting model partitions the residual risk of each crime and disorder type into one spatial shared component, one temporal shared component, and type-specific spatial, temporal, and space–time components. The shared components capture the underlying spatial pattern and time trend common to all types of crime and disorder. Results show that population size, residential mobility, and the central business district are positively associated with all outcomes. The spatial shared component is found to explain the largest proportion of residual variability for all types of crime and disorder. Spatiotemporal hotspots of crime and disorder are examined to contextualize broken windows theory. Applications of multivariate spatiotemporal modeling with shared components to ecological crime theories and crime prevention policy are discussed.
KW - spatio-temporal
KW - multivariate
KW - shared component
KW - crime and disorder
KW - Bayesian hierarchical model
U2 - 10.1111/gean.12173
DO - 10.1111/gean.12173
M3 - Article
VL - 51
SP - 221
EP - 248
JO - Geographical Analysis
JF - Geographical Analysis
SN - 0016-7363
IS - 2
ER -