Crime Prediction with Graph Neural Networks and Multivariate Normal Distributions
This work addresses the need for more detailed crime prediction for public safety applications, representing an incremental advancement in spatiotemporal modeling.
The paper tackles the problem of low-resolution crime prediction by introducing a new architecture that combines graph convolutional networks and multivariate Gaussian distributions for high-resolution spatiotemporal forecasting, achieving the best validation and test scores among baseline models with significant improvements.
Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions. This paper introduces a new architecture with the graph convolutional networks (GCN) and multivariate Gaussian distributions to perform high-resolution forecasting that applies to any spatiotemporal data. We tackle the sparsity problem in high resolution by leveraging the flexible structure of GCNs and providing a subdivision algorithm. We build our model with Graph Convolutional Gated Recurrent Units (Graph-ConvGRU) to learn spatial, temporal, and categorical relations. In each node of the graph, we learn a multivariate probability distribution from the extracted features of GCNs. We perform experiments on real-life and synthetic datasets, and our model obtains the best validation and the best test score among the baseline models with significant improvements. We show that our model is not only generative but also precise.