Fully model-based approaches for spatially misaligned data

Journal of the American Statistical Association

Volumn 95, Issue 451, 2000, Pages 877-887

  Mugglin, Andrew S ^a   Carlin, Bradley P ^b   Gelfand, Alan E ^c  

^a OHIO STATE UNIVERSITY   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

^c UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

Areal interpolation; Bayesian methods; Environmental risk analysis; Geographic information system; Markov chain Monte Carlo

Indexed keywords

EID: 2242462362     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.2000.10474279     Document Type: Article

References (33)

1. Bernardinelli, L., Clayton, D. G., and Montomoli, C. (1995), “Bayesian Estimates of Disease Maps: How Important are Priors?,” Statistics in Medicine, 14, 2411-2431.
2. Bernardinelli, L., and Montomoli, C. (1992), “Empirical Bayes Versus Fully Bayesian Analysis of Geographical Variation in Disease Risk,” Statistics in Medicine, 11, 983-1007.
3. Besag, J. (1974), “Spatial Interaction and the Statistical Analysis of Lattice Systems” (with discussion), Journal of the Royal Statistical Society, Ser. B, 36, 192-236.
4. Besag, J., Green, P., Higdon, D., and Mengersen, K. (1995), “Bayesian Computation and Stochastic Systems” (with discussion), Statistical Science., 10, 3-66.
5. Best, N. G., Ickstadt, K., Wolpert, R. L., Cockings, S., Elliott, P., Bennett, J., Bottle, A., and Reed, S. (2000), “Modeling the Impact of Traffic-Related Air Pollution on Childhood Respiratory Illness” (with discussion), in Case Studies in Bayesian Statistics, Vol. 5, eds. C. Gatsonis, R. E. Kass, B. P. Carlin, A. L. Carriquiry, A. Gelman, I. Verdinelli, and M. West, New York: Springer-Verlag.
6. Carlin, B. P., and Louis, T. A. (1996), Bayes and Empirical Bayes Methods for Data Analysis, Boca Raton, FL: Chapman and Hall/CRC Press.
7. Cressie, N. A. C. (1993), Statistics for Spatial Data (2nd ed.), New York: Wiley.
8. Devine, O. J., Quakers, J. R., Morrissey, J. L., and Wall, P. A. (1998), “Estimation of the Impact of the Former Feed Materials Production Center (FMPC) on Lung Cancer Mortality in the Surrounding Community,” technical report, Radiation Studies Branch, Division of Environmental Hazards and Health Effects, National Center for Environmental Health, Centers for Disease Control and Prevention.
9. Fisher, P. F., and Langford, M. (1995), “Modeling the Errors in Areal Interpolation Between Zonal Systems Using Monte Carlo Simulation,” Environment and Planning A, 27, 211-224.
10. Flowerdew, R., and Green, M. (1989), “Statistical Methods for Inference Between Incompatible Zonal Systems,” in Accuracy of Spatial Databases, eds. M. F. Goodchild and S. Gopal, London: Taylor and Francis, pp. 239-248.
11. Flowerdew, R., and Green, M. (1991), “Data Integration: Statistical Methods for Transferring Data Between Zonal Systems,” in Handling Geographical Information: Methodology and Potential Applications, eds. I. Masser and M. Blakemore, Essex, U.K.: Longman, Harlow, pp. 38-54.
12. Flowerdew, R., and Green, M. (1992), “Developments in Areal Interpolating Methods and GIS,” Annals of Regional Science, 26, 67-78.
13. Flowerdew, R., and Green, M. (1994), “Areal Interpolation and Types of Data,” in Spatial Analysis and GIS, eds. S. Fotheringham and P. Rogerson, London: Taylor and Francis, pp. 121-145.
14. Flowerdew, R., Green, M., and Kehris, E. (1991), “Using Areal Interpolation Methods in Geographic Information Systems,” Papers in Regional Science, 70, 303-315.
15. Gelfand, A. E., and Ghosh, S. K. (1998), “Model Choice: A Minimum Posterior Predictive Loss Approach,” Biometrika, 85, 1-11.
16. Gelman, A., Roberts, G. O., and Gilks, W. R. (1996), “Efficient Metropolis Jumping Rules,” in Bayesian Statistics 5, eds. J. M. Bernardo, J. O.
17. Berger, A. P. Dawid, and A. F. M. Smith, Oxford, U.K.: Oxford University Press, pp. 599-607.
18. Gelman, A., and Rubin, D. B. (1992), “Inference From Iterative Simulation Using Multiple Sequences” (with discussion), Statistical Science, 7, 457-511.
19. Geyer, C. J., and Thompson, E. A. (1995), “Annealing Markov Chain Monte Carlo With Applications to Ancestral Inference,” Journal of the American Statistical Association, 90, 909-920.
20. Goodchild, M. F., Ansellin, L., and Deichmann, U. (1993), “A Framework for the Areal Interpolation of Socioeconomic Data,” Environment and Planning A, 25, 383-387.
21. Goodchild, M. F., and Lam, N. S.-N. (1980), “Areal Interpolation: A Variant of the Traditional Spatial Problem,” Geoprocessing, 1, 297-312.
22. Killough, G. G., Case, M. J., Meyer, K. R., Moore, R. E., Rope, S. K., Schmidt, D. W., Schleien, B., Sinclair, W. K., Voilleque, P. G., and Till, J. E. (1996), “Task 6: Radiation Doses and Risk to Residents from FMPC Operations from 1951-1988,” draft report, Radiological Assessments Corporation, Neeses, SC.
23. Lam, N. S.-N. (1983), “Spatial Interpolation Methods: A Review,” American Cartographer, 10, 129-149.
24. Langford, M., Maguire, D. J., and Unwin, D. J. (1991), “The Areal Interpolation Problem: Estimating Population Using Remote Sensing in a GIS Framework,” in Handling Geographical Information: Methodology and Potential Applications, eds. I. Masser and M. Blakemore, Essex, U.K.: Longman, Harlow, pp. 55-77.
25. Liu, J. S., Wong, W. H., and Kong, A. (1994), “Covariance Structure of the Gibbs Sampler With Applications to the Comparisons of Estimators and Augmentation Schemes,” Biometrika, 81, 27-30.
26. Mugglin, A. S. (1999), “Fully Model-Based Approaches for Spatially Misaligned Data,” unpublished Ph.D. dissertation, University of Minnesota, Division of Biostatistics.
27. Mugglin, A. S., and Carlin, B. P. (1998), “Hierarchical Modeling in Geographic Information Systems: Population Interpolation Over Incompatible Zones,” Journal of Agricultural, Biological, and Environmental Statistics, 3, 111-130.
28. Mugglin, A. S., Carlin, B. P., Zhu, L., and Conlon, E. (1999), “Bayesian Areal Interpolation, Estimation, and Smoothing: An Inferential Approach for Geographic Information Systems,” Environment and Planning A, 31, 1337-1352.
29. Rogers, J. F., and Killough, G. G. (1997), “Historical Dose Reconstruction Project: Estimating the Population at Risk,” Health Physics, 72, 186-194.
30. Spiegelhalter, D. J., Best, N., and Carlin, B. P. (1998), “Bayesian Deviance, the Effective Number of Parameters, and the Comparison of Arbitrarily Complex Models,” Research Report 98-009, University of Minnesota, Division of Biostatistics.
31. Tobler, W. R. (1979), “Smooth Pycnophylactic Interpolation for Geographical Regions” (with discussion), Journal of the American Statistical Association, 74, 519-536.
32. Wakefield, J., and Morris, S. (1997), “The Bayesian Modeling of Disease Risk in Relation to a Point Source,” technical report, Imperial College, London, Dept, of Epidemiology and Public Health.
33. Zhu, L., Carlin, B. P., English, P., and Scalf, R. (2000), “Hierarchical Modeling of Spatio-Temporally Misaligned Data: Relating Traffic Density to Pediatric Asthma Hospitalizations,” Environmetrics, 11, 43-61.