  We describe an approach for identifying groups of dynamically similar
locations in spatial time-series data based on a simple Markov transition
model. We give maximum-likelihood, empirical Bayes, and fully Bayesian
formulations of the model, and describe exhaustive, greedy, and MCMC-based
inference methods. The approach has been employed successfully in several
studies to reveal meaningful relationships between environmental patterns and
disease dynamics.
