ABSTRACT
Discrete-valued spatio-temporal data arise frequently across a diverse range of subject-matter disciplines, including epidemiology, small area estimation in federal surveys, environmental science, and ecology, among others. In general, modeling this type of data can prove challenging due to the complexity of the observed data and underlying dynamical processes (e.g., see Cressie and Wikle, 2011, and the references therein). In this chapter, we focus primarily on modeling count data using spatio-temporal generalized linear models within a Bayesian hierarchical modeling (BHM) framework. In particular, we review some of the common methods in this context and describe some recent advances. For completeness, we provide brief discussion surrounding other types of discrete-valued spatio-temporal data, such as Bernoulli data and others. Finally, we provide a succinct real data illustration outlining the prediction of waterfowl migratory patterns across the north-central United States and Canada.
