Bayesian estimation and prediction for inhomogeneous spatiotemporal log-gaussian cox processes using low-rank models, with application to criminal surveillance

In this article, we propose a method for conducting likelihood-based inference for a class of nonstationary spatiotemporal log-Gaussian Cox processes. The method uses convolution-based models to capture spatiotemporal correlation structure, is computationally feasible even for large datasets, and does not require knowledge of the underlying spatial intensity of the process.We describe an application to a surveillance system for detecting emergent spatiotemporal clusters of homicides in Belo Horizonte, Brazil, and discuss the advantages and drawbacks of our model-based approach by comparison with other spatiotemporal surveillance methods that have been proposed in the literature.