Need to go further: using INLA to discover limits and chances of burglaries’ spatiotemporal prediction in heterogeneous environments

Abstract

Abstract Near-repeat victimization patterns have made predictive models for burglaries possible. While the models have been implemented in different countries, the results obtained have not always been in line with initial expectations; to the point where their real effectiveness has been called into question. The ability to predict crime to improve preventive policing strategies is still under study. This study aims to discover the limitations to and the success of the models that attempt to predict burglaries based on spatiotemporal patterns of the risk of break-ins spreading in geographic proximity to the initial break-ins. A spatiotemporal log-Gaussian Cox process is contemplated to model the generic near-repeat victimization scenario and adjusted using the Integrated Nested Laplace Approximation (INLA) methodology. This approach is highly suitable for studying and describing the near-repeat phenomenon. However, predictions obtained with INLA are quite monotonous, of low variability and do not reproduce well the local and short-term dynamics of burglaries for predictive purposes. The conclusion is that predictive models cannot be restricted exclusively to distance decay risk, but they must be designed to detect other types of spatiotemporal patterns which, among other possibilities, open up the possibility of correlating distant events and clusters. Although other studies have already highlighted this problem, the proposal here is to go one step further and clearly extend the near-repeat spatial patterns to achieve better prediction results.

Keywords

• Spatiotemporal patterns
• Log-Gaussian Cox process
• INLA
• Predictive policing
• Near-repeat victimization