Exploring non-stationarity of local mechanism of crime events with spatial-temporal weighted regression

For a more effective understanding of dynamic of mechanism and cluster of local crime, this study uses kernel density to reveal abilities of detecting space-time hotspots in the context of time geography. Since spatial data are correlated in nature, geographically weighted regression (GWR) has been proven as an effective tool to address the spatial non-stationarity. Thus, this study adopts temporal variants to detect the spatial-temporal non-stationarity of structural measures simultaneously. Using a geocoded criminal dataset of residential burglary in Da-an District of Taipei City from 1999 to 2007, we examine the proposed framework allowing interactively 3-D visualization of crime hotspots by volume rendering. We also reveal the non-stationarity of estimations of social structural measures by a variant weighted regression approach. Emphasizing the supplementary aspect of our embedded framework, we conclude that 3-D spatial-temporal data analysis and the variant of geographically weighted regression could identify the space-time hotspots as well as extract and interpret the spatial-temporal non-stationarity of mechanism of residential burglary.