Neural Point Process for Learning Spatiotemporal Event Dynamics

Learning the dynamics of spatiotemporal events is a fundamental problem. Neural point processes enhance the expressivity of point process models with deep neural networks. However, most existing methods only consider temporal dynamics without spatial modeling. We propose Deep Spatiotemporal Point Process (\ours{}), a deep dynamics model that integrates spatiotemporal point processes. Our method is flexible, efficient, and can accurately forecast irregularly sampled events over space and time. The key construction of our approach is the nonparametric space-time intensity function, governed by a latent process. The intensity function enjoys closed form integration for the density. The latent process captures the uncertainty of the event sequence. We use amortized variational inference to infer the latent process with deep networks. Using synthetic datasets, we validate our model can accurately learn the true intensity function. On real-world benchmark datasets, our model demonstrates superior performance over state-of-the-art baselines. Our code and data can be found at the https://github.com/Rose-STL-Lab/DeepSTPP

A dynamic microsimulation framework for generating synthetic spatiotemporal crime patterns

The significance of synthetic crime datasets in criminological research cannot be underestimated, as real crime datasets are usually unavailable in many policing jurisdictions, due to reasons such as privacy concerns and the lack of shareable data formats. This study introduces a dynamic microsimulation framework by which a specified spatiotemporal crime pattern can be synthesised. A case study presented compares a real crime dataset with the synthesised datasets, and found certain spatiotemporal similarities between them. The developed model has the potential for wider applications in criminology, given some identified areas of improvement