RT Article
T1 Bayesian Spatio-Temporal Modeling for Analysing Local Patterns of Crime Over Time at the Small-Area Level
JF Journal of quantitative criminology
VO 30
IS 1
SP 57
OP 78
A1 Law, Jane
A2 Quick, Matthew 1973-
A2 Chan, Ping
LA English
YR 2014
UL https://krimdok.uni-tuebingen.de/Record/1764276892
AB Objectives Explore Bayesian spatio-temporal methods to analyse local patterns of crime change over time at the small-area level through an application to property crime data in the Regional Municipality of York, Ontario, Canada. Methods This research represents the first application of Bayesian spatio-temporal modeling to crime trend analysis at a large map scale. The Bayesian model, fitted by Markov chain Monte Carlo simulation using WinBUGS, stabilized risk estimates in small (census dissemination) areas and controlled for spatial autocorrelation (through spatial random effects modeling), deprivation, and scarce data. It estimated (1) (linear) mean trend; (2) area-specific differential trends; and (3) (posterior) probabilities of area-specific differential trends differing from zero (i.e. away from the mean trend) for revealing locations of hot and cold spots. Results Property crime exhibited a declining mean trend across the study region from 2006 to 2007. Variation of area-specific trends was statistically significant, which was apparent from the map of (95 % credible interval) differential trends. Hot spots in the north and south west, and cold spots in the middle and east of the region were identified. Conclusions Bayesian spatio-temporal analysis contributes to a detailed understanding of small-area crime trends and risks. It estimates crime trend for each area as well as an overall mean trend. The new approach of identifying hot/cold spots through analysing and mapping probabilities of area-specific crime trends differing from the mean trend highlights specific locations where crime situation is deteriorating or improving over time. Future research should analyse trends over three or more periods (allowing for non-linear time trends) and associated (changing) local risk factors.
K1 Spatial
K1 Spatio-temporal
K1 Bayesian hierarchical models
K1 Hot Spots
K1 Crime trends
K1 Probability mapping
DO 10.1007/s10940-013-9194-1