— 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...
Christian Ivaha, Hasan Al-Madfai, Gary Higgs, J. A