Spatio-Temporal Crime Prediction with Temporally Hierarchical Convolutional Neural Networks

In this paper, we propose a new deep learning based model that uses convolutional neural networks for spatiotemporal crime prediction. To learn the temporal pattern of crime events, we employ a temporally hierarchical structure that branches along the temporal dimension. In addition, channel projection is applied to capture the separate influences of crime events over future crime risk. In the results section, our model is compared with classical methods and the performance is analyzed on publicly available Chicago and Los Angeles crime datasets. The proposed model significantly improves the performance compared to the traditional methods.