| Abstract: | Being able to anticipate crime such that new crime events can be dealt with effectively or prevented entirely, leads police forces worldwide to look at applying predictive policing, which provides predictions of times and places at risk for crime, such that proactive preventative measures can be taken. Ideally, predictive policing models predict crime at a high spatio-temporal level, while also providing optimal prediction performance. The main objective of this paper is therefore to evaluate the impact of varying grid resolution, temporal resolution and historical time frame on prediction performance. To investigate this, we analyse home burglary data from a large city in Belgium and predict new crime events using a range of parameter values, comparing the resulting prediction performances. Given the potential prediction performance costs associated with prediction at a high spatio-temporal resolution, consideration should be given to balance practical requirements with performance requirements. |
