Investigating 3D-STDenseNet for Explainable Spatial Temporal Crime Forecasting

Crime is a well-known social problem faced worldwide. With the availability of large city datasets, the scientific community for predictive policing has switched its focus from people-centric to place-centric, focusing on heterogeneous data points related to a particular geographic region in predicting crimes. Such data-driven techniques identify micro-level regions known as hotspots with high crime intensity. In this paper, we adapt the state-of-the-art spatial-temporal prediction model STDenseNetFus to predict crime in geographic regions in the presence of external factors such as a region’s demographics, seasonal events and weather. We demonstrate that STDenseNet maintains prediction performance compared to previous results [1] on the same dataset despite significantly reduced parameter count. We further extend STDenseNetFus architecture from two-dimensional to three-dimensional convolutions and show that it further improves the prediction results. Finally we investigate the use of the DeepShap model explanation method to provide insights into the important input features effecting the model forecasts.