Bayesian spatial random effect modeling for analyzing burglary risks controlling for offender, socioeconomic, and unknown risk factors
Authors: Jane Law and Ping W. Chan
Overview
Abstract (English)
This paper adopts a Bayesian spatial random effect modelling approach to analyse the risk of domestic burglary in Cambridgeshire, England, at the census output area level (OA). The model, in the form of Binomial spatial logistic regression, integrates offence and offender based theories and takes into account unknown local risk factors (represented as unexplained spatial autocorrelation in the model). A score of ‘proximity to offenders’ was calibrated for each OA based on the number of likely offenders in the county, the OAs they reside, and their proximities. Our results indicate that areas that have a score higher than the average score were at higher risks of being burgled. Household occupied by non-couple and economically inactivity are positively associated confounders. Household occupied by owner is a negatively associated confounder. These confounders diminish the effect of high score of proximity to offenders, which, however, remains positively associated with the risk of burglary. Bayesian spatial random effect modelling, which adds to the traditional (non-spatial) regression model a spatial random effect term, stabilizes estimated risks and remarkably improves model fit and causation inference. Mapping the results of spatial random effect reveals locations of high risk of burglary after controlling for offender and socioeconomic factors. Limitations of the study and strategies to deter burglaries based on the results of spatial random effect modelling are discussed.
Abstract (French)
Please note that abstracts only appear in the language of the publication and might not have a translation.
Details
Type | Journal article |
---|---|
Author | Jane Law and Ping W. Chan |
Publication Year | 2011 |
Title | Bayesian spatial random effect modeling for analyzing burglary risks controlling for offender, socioeconomic, and unknown risk factors |
Volume | 5 |
Journal Name | Applied Spatial Analysis and Policy |
Number | 1 |
Pages | 73-96 |
Publication Language | English |
- Jane Law
- Jane Law and Ping W. Chan
- Bayesian spatial random effect modeling for analyzing burglary risks controlling for offender, socioeconomic, and unknown risk factors
- Applied Spatial Analysis and Policy
- 5
- 2011
- 1
- 73-96