The Dynamic Spatial Disaggregation Approach: A Spatio-Temporal Modelling of Crime

The spatio-temporal modelling and forecasting of incidences of crimes have now become a routine part of crime prevention operations. However, obtaining reliable forecasts for cases of changing spatial boundaries using models which take the influences of exogenous factors on the spatial and temporal dynamics of crime into account remains a challenge. The proposed Dynamic Spatial Distribution Approach (DSDA) is a modelling approach that provides spatio-temporal forecasts that incorporate the influences of salient weather conditions that have a bearing on crime dynamics at both the temporal and spatial levels. The DSDA starts with modelling and forecasting the dataset of the entire area of interest as a whole. Given reliable forecasts, the DSDA spatially disaggregates them by extrapolating the spatial patterns identified from the historic data. This is carried out by firstly identifying the spatial pattern and then defining a set of weights, one for each of the clusters making up this pattern. A number of methods to determining these weights, including a benchmark, univariate and causal approaches are proposed. The DSDA was developed within a GIS and classical statistics environment. Applied to the daily number of criminal damage incidences in the City of Cardiff, UK, the DSDA yielded a city-wide forecast error of 3.51 crimes per day. The spatial disaggregation using weights identified using Least Squares estimates of expected crime percentages in each cluster yielded an average of 1.1240 crimes per day per cluster. The model used incorporated the influences of weekdays and ambient conditions on the spatial distribution of crime