Hierarchical Bayesian modeling of spatio-temporal area-interaction processes

To model spatial point patterns with discrete time stamps a flexible spatio-temporal area-interaction point process is proposed. In particular, this model is suitable for describing the dependency between point patterns over time, when the new point pattern arises from the previous point pattern. A hierarchical model is also implemented in order to incorporate the underlying evolution process of the model parameters. For parameter estimation, a double Metropolis-Hastings within Gibbs sampler is used. The performance of the estimation algorithm is evaluated through a simulation study. Finally, the point pattern forecasting procedure is demonstrated through a simulation study and an application to United States natural caused wildfire data from 2002 to 2019. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study introduces a flexible spatio-temporal area-interaction point process model for spatial point patterns with discrete time stamps, which is suitable for describing the dependency between point patterns over time. A hierarchical model is implemented to incorporate the underlying evolution process of model parameters, and a double Metropolis-Hastings within Gibbs sampler is used for parameter estimation. The performance of the estimation algorithm is evaluated through simulation studies.