Citywide Multi-Step Crime Prediction via Context-Aware Bayesian Tensor Decomposition

Crime prediction, which focuses on forecasting the occurrence of criminal activities across city regions before they occur, constitutes an essential capability of surveillance systems designed to enhance urban security. While much effort has been invested in this field, most of the existing studies pay little attention to the influence of situational contexts on criminal activities, which hinders further improvement in prediction performance. To address this challenge, we propose a novel context-aware Bayesian tensor decomposition framework, namely cBTD-Crime, for citywide multi-step crime prediction. More specifically, cBTD-Crime first constructs a third-order tensor to simultaneously model spatial, temporal, and contextual factors and then applies the CP decomposition to exploit the intricate relationships between the three factors to facilitate the prediction process. To reduce the parameter tuning cost, cBTD-Crime further reformulates the problem from a probabilistic perspective, where a range of carefully selected distributions are placed on the spatial, temporal, and contextual latent factors. Finally, an efficient Gibbs sampling procedure is developed to generate a series of samples and the arithmetic mean is computed to obtain the predicted number of crime incidents. Experimental results show that cBTD-Crime achieves superior performance on real-world crime datasets in terms of different evaluation metrics.