Bayesian spatial models

Bayesian spatial models are statistical tools used to analyze data that varies across different locations. They combine existing knowledge (prior beliefs) with observed data to estimate how variables change in space, accounting for uncertainty. This approach helps identify patterns or trends in geographical data, such as disease outbreaks or environmental changes, by updating predictions as new information becomes available. Essentially, they provide a flexible way to understand spatial relationships while explicitly considering uncertainty, leading to more informed and reliable insights into spatial phenomena.