Mapping rates associated with polygons

Abstract.

Suppose that geographic data under investigation are rates associated with polygons. For example, disease incidence, mortality, and census undercount data may be displayed as rates. Spatial analysis of data of this sort can be handled very naturally through Bayesian hierarchical statistical modeling, where there is a measurement process at the first level, an explanatory process at the second level, and a prior probability distribution on unknowns at the third level. In our paper, we shall feature epidemiological data, specifically disease-incidence rates, and the “polygons” referred to in the title are typically states or counties.