Fully model-based approaches for spatially misaligned data
We consider inference using multivariate data that are spatially misaligned; that is, involving variables (typically counts or rates) that are aggregated over differing sets of regional boundaries. Geographic information systems enable the simultaneous display of such datasets, but their current capabilities are essentially only descriptive, not inferential. We describe a hierarchical modeling approach that provides a natural solution to this problem through its ability to sensibly combine information from several sources of data and available prior information. Illustrating in the context of counts, allocation under nonnested regional grids is handled using conditionally independent Poisson-mullinomial models. Explanatory covariales and multilevel responses are also easily accommodated, with spatial correlation modeled using a conditionally autoregressive prior structure. Methods for dealing with missing values in spatial “edge zones” are also discussed. Like many recent hierarchical Bayesian applications, computing is implemented via a carefully tailored Metropolis-Hastings algorithm. We illustrate our method with a complex dataset involving inhalation exposure to radon emanating from a depleted uranium fuel processing plant in southwestern Ohio. Structure counts (obtained from U.S. Geological Survey topographical maps) are used to realign sex- and age group-specific U.S. census block group population counts onto a 160-cell circular “windrose” centered at the plant. © 2000 Taylor & Francis Group, LLC.
Duke Scholars
Altmetric Attention Stats
Dimensions Citation Stats
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 3802 Econometrics
- 1603 Demography
- 1403 Econometrics
- 0104 Statistics
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 3802 Econometrics
- 1603 Demography
- 1403 Econometrics
- 0104 Statistics