ABSTRACT
The concept of spatial neighborhood or proximity matrix is useful in the exploration of areal data. A popular spatial model is the Besag-York-Mollie model which takes into account that data may be spatially correlated and observations in neighboring areas may be more similar than observations in areas that are farther away. The spatial random effect is assigned a Conditional Autoregressive distribution which smoothes the data according to a certain neighborhood structure that specifies that two areas are neighbors to the share a common boundary. The chapter shows how to compute neighborhood matrices, expected counts, and standardized incidence ratios (SIRs). SIRs may be misleading and insufficiently reliable for reporting, and it is preferred to estimate disease risk by using Bayesian hierarchical models that enable to borrow information from neighboring areas and incorporate covariates information resulting in the smoothing or shrinking of extreme values.
