  In this paper, we perform Bayesian Inference to analyze spatial tree count
data from the Timiskaming and Abitibi River forests in Ontario, Canada. We
consider a Bayesian Generalized Linear Geostatistical Model and implement a
Markov Chain Monte Carlo algorithm to sample from its posterior distribution.
How spatial predictions for new sites in the forests change as the amount of
training data is reduced is studied and compared with a Logistic Regression
model without a spatial effect. Finally, we discuss a stratified sampling
approach for selecting subsets of data that allows for potential better
predictions.
