Mapping rates associated with polygons

Noel Cressie, Hal S. Stern and Deanne Reber Wright
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Noel Cressie: Department of Statistics, 1958 Neil Avenue, 404 Cockins Hall, The Ohio State University, Columbus, OH 43210-1247, USA (e-mail: ncressie@stat.ohio-state.edu)
Hal S. Stern: Department of Statistics, Iowa State University, Ames, IA 50011-1210, USA (e-mail: hstern@iastate.edu)
Deanne Reber Wright: Pioneer Hi-Bred International, Inc., 7300 NW 62 Avenue, Johnston, IA 50131, USA (e-mail: wrightdean@phibred.com)

Journal of Geographical Systems, 2000, vol. 2, issue 1, 69 pages

Abstract: 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.

Keywords: Key words: Bayesian inference; disease mapping; hierarchical models; loss functions; spatio-temporal models (search for similar items in EconPapers)
Date: 2000
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DOI: 10.1007/s101090050031

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