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Using Geostatistical Methods in the Analysis of Public Health Data: The Final Frontier?

  • Linda J. Young
  • Carol A. Gotway
Chapter
Part of the Quantitative Geology and Geostatistics book series (QGAG, volume 16)

Abstract

Geostatistical methods have been demonstrated to be very powerful analytical tools in a variety of disciplines, most notably in mining, agriculture, meteorology, hydrology, geology and environmental science. Unfortunately, their use in public health, medical geography, and spatial epidemiology has languished in favor of Bayesian methods or the analytical methods developed in geography and promoted via geographic information systems. In this presentation, we provide our views concerning the use of geostatistical methods for analyzing spatial public health data. We revisit the geostatistical paradigm in light of traditional analytical examples from public health. We discuss the challenges that need to be faced in applying geostatistical methods to the analysis of public health data as well as the opportunities for increasing the use of geostatistical methods in public health applications.

Keywords

Multivariate Gaussian Distribution Multivariate Distribution Geostatistical Method Spatial Neighborhood Census Tract Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The senior author was partially supported by the Florida Department of Health, Division of Environmental Health and Grant/Cooperative Agreement Number 5 U38 EH000177-02 from the Centers for Disease Control and Prevention (CDC). The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

References

  1. Brunsdon CF, Fotheringham AS, Charlton ME (1996) Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28:281–298CrossRefGoogle Scholar
  2. Centers for Disease Control and Prevention (CDC) (2006) Health risks in the United States: behavioral risk factor surveillance system 2006. US Department of Health and Human Services, CDC, Atlanta, GAGoogle Scholar
  3. Diggle PJ, Robeiro PJ (2007) Model-based geostatistics. Springer, New YorkGoogle Scholar
  4. Diggle PJ, Tawn JA, Moyeed RA (1998) Model based geostatistics. Appl Stat 47:299–350Google Scholar
  5. Gelfand AE, Kim H-J, Sirmans CF, Banerjee S (2003) Spatial modeling with spatially varying coefficient processes. J Am Stat Assocn 98:387–396CrossRefGoogle Scholar
  6. Goovaerts P (2005) Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging. Int J Health Geogr 4:31CrossRefGoogle Scholar
  7. Goovaerts P (2008) Geostatistical analysis of health data: state-of-the-art and perspective. In: Soares A, Pereira MJ, Dimitrakopoulos R (eds) geoENV VI –geostatistics for environmental applications. Springer, The Netherlands, pp 3–22.CrossRefGoogle Scholar
  8. Gotway CA, Stroup WW (1997) A generalized linear model approach to spatial data analysis and prediction. J Agric, Biol, Environ Stat 2:157–178CrossRefGoogle Scholar
  9. Gotway CA, Wolfinger RD (2003) Spatial prediction of counts and rates. Stat Med 22:1415–1432CrossRefGoogle Scholar
  10. Gotway CA, Young LJ (2002) Combining incompatible spatial data. J Am Stat Assoc 97:632–648CrossRefGoogle Scholar
  11. Gotway CA, Young LJ (2007) A geostatistical approach to linking spatially-aggregated data from different sources. J Comput Graph Stat 16:115–135CrossRefGoogle Scholar
  12. Hastie TJ, Tibshirani RJ (1993) Varying-coefficient models. J R Stat Soc B 55:757–796Google Scholar
  13. Mardia KV (1970) Families of bivariate distributions. Hafner, Darienn, CTGoogle Scholar
  14. McNeill L (1991) Interpolation and smoothing of binomial data for the Southern African Bird Atlas Project. S Afr J Stat 25:129–136Google Scholar
  15. Mockus A (1998) Estimating dependencies from spatial averages. J Comput Graph Stat 7:501–513Google Scholar
  16. Monestiez P, Dubroca L, Bonnin E, Durbec JP, Guinet C (2005) Comparison of model based geostatistical methods in ecology: application to fin whale spatial distribution in northwestern Mediterranean Sea. In: Leuangthong O, Deutsch CV (eds) Geostatistics Banf 2005, vol. 2. Kluwer, Dordrecht, The Netherlands, pp 777–786Google Scholar
  17. Monestiez P, Dubroca L, Bonnin E, Durbec J-P, Guinet C (2006) Geostatistical modeling of spatial distribution of Balaenoptera physalus in the Northwestern Mediterranean Sea from sparse count data and heterogeneous observations efforts. Ecol Model 193:615–628CrossRefGoogle Scholar
  18. Müller WG (1998) Fundamentals of spatial statistics. In: Collecting spatial data: optimum design of experiments for random fields. Physica-Verlag, HeidelbergGoogle Scholar
  19. Nakaya T, Fotheringham AS, Brunsdon C, Charlton M (2005) Geographically weighted Poisson regression for disease association mapping. Stat Med 24:2695–2717CrossRefGoogle Scholar
  20. Rivoirard J (1994) Introduction to disjunctive kriging and non-linear geostatistics. Clarendon, OxfordGoogle Scholar
  21. Schabenberger O, Gotway CA (2005) Statistical methods for spatial data analysis. CRC Press, Boca Raton, FLGoogle Scholar
  22. Tobler W (1979) Smooth pycnophylactic interpolation for geographical regions (with discussion). J Am Stat Assoc 74:519–536CrossRefGoogle Scholar
  23. U.S. Census Bureau (2001) Age: 2000. Economics and statistics administration. U.S. Department of Commerce, Washington, DCGoogle Scholar
  24. Waller LA, Zhu L, Gotway CA, Gorman D, Gruenewald P (2007) Quantifying geographic variations in associations between alcohol distribution and violence: a comparison of geographically weighted regression and spatially varying coefficient models. Stoch Environ Res Risk Assess 21:573–588CrossRefGoogle Scholar
  25. Wheeler D, Tiefelsdorf M (2005) Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst 7:161–187CrossRefGoogle Scholar
  26. World Health Organization (2005) International classification of diseases and related health problems (ICD-10), 2nd edn. WHOGoogle Scholar
  27. Young LJ, Gotway CA, Yang J, Kearney G, DuClos C (2008) Assessing the association between environmental impacts and health outcomes: a case study from Florida. Stat Med (in Press). doi: 10.1002/sim.3249Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of FloridaGainesvilleUSA

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