Binary Spatio-Temporal Process for Point-Referenced Data using a Markov Random Field

Music City Center 

In this talk, we propose a novel approach for modeling spatio-temporal binary data with point-referenced data by leveraging Markov kernels to capture both spatial and temporal dependencies. Spatio-temporal binary data, which commonly arises in fields such as environmental science, epidemiology, and geospatial analysis, usually relies on a computationally cumbersome latent Gaussian process in the context of point-referenced applications to overcome the problem of specifying dependencies on a continuous spatial structure. Through the use of Markov kernels, we provide a flexible mechanism for incorporating spatial and temporal correlations in a unified manner, allowing for non-linear dependencies and varying transition probabilities over both space and time. Our approach is validated with an application on forest cover in the CONUS using the Forest Inventory Analysis data and the Tree Canopy Cover data product from the Forest Service, demonstrating its ability to accurately capture spatio-temporal dynamics and improve prediction accuracy over existing methods. The results show that Markov kernels offer a powerful and scalable framework for analyzing spatio-temporal binary data.

Spatio-temporal analysis

Binary Data

Markov Random Field

Bayesian statistics 

Section on Statistics and the Environment