A dynamic spatio-temporal model for spatial data

Spatial Statistics
By: , and 



Analyzing spatial data often requires modeling dependencies created by a dynamic spatio-temporal data generating process. In many applications, a generalized linear mixed model (GLMM) is used with a random effect to account for spatial dependence and to provide optimal spatial predictions. Location-specific covariates are often included as fixed effects in a GLMM and may be collinear with the spatial random effect, which can negatively affect inference. We propose a dynamic approach to account for spatial dependence that incorporates scientific knowledge of the spatio-temporal data generating process. Our approach relies on a dynamic spatio-temporal model that explicitly incorporates location-specific covariates. We illustrate our approach with a spatially varying ecological diffusion model implemented using a computationally efficient homogenization technique. We apply our model to understand individual-level and location-specific risk factors associated with chronic wasting disease in white-tailed deer from Wisconsin, USA and estimate the location the disease was first introduced. We compare our approach to several existing methods that are commonly used in spatial statistics. Our spatio-temporal approach resulted in a higher predictive accuracy when compared to methods based on optimal spatial prediction, obviated confounding among the spatially indexed covariates and the spatial random effect, and provided additional information that will be important for containing disease outbreaks.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title A dynamic spatio-temporal model for spatial data
Series title Spatial Statistics
DOI 10.1016/j.spasta.2017.02.005
Volume 20
Year Published 2017
Language English
Publisher Elsevier
Contributing office(s) Coop Res Unit Seattle, National Wildlife Health Center
Description 15 p.
First page 206
Last page 220
Country United States
State Wisconsin
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