Residential burglary target selection: An analysis at the property-level using Google Street View


Objectives: To quantitatively test findings from offender-based literature—primarily consisting of small-sample interviews or experimental scenarios with convicted burglars—to investigate the extent to which the physical attributes of residential homes and their immediate surrounding area contribute to the risk of burglary. Methods: Collecting fresh, micro-level data using Google Street View (GSV) as a tool of Systematic Social Observation (SSO), we utilise a case-control design to isolate property-level effects. Analysis is carried out using conditional logistic regression. Results: The ease of escape from a property, the extent to which the dwelling is accessible, and the extent to which it is closed to surveillance from neighbours and passers-by are all positively related to burglary risk. There is no evidence that indications of resident wealth are related to the likelihood of victimisation, or that the effect of surveillability varies depending on the extent of collective efficacy in a neighbourhood. Conclusions: Burglary target selection does not stop at the selection of a target neighbourhood, but certain characteristics of individual properties within the same neighbourhood are in turn indicative of burglary risk. Quantitative analyses partly support findings from the offender-based research on residential burglary. We encourage future research to consider using GSV as a method of collecting fresh data, with the broader aim of explaining criminal behaviour at micro-spatial scales.

Applied Geography