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Design and Analysis for Modeling and Predicting Spatial Contamination

Mathematical Geology volume 31, pages 1–22 (1999)Cite this article

Abstract

Sampling and prediction strategies relevant at the planning stage of the cleanup of environmental hazards are discussed. Sampling designs and models are compared using an extensive set of data on dioxin contamination at Piazza Road, Missouri. To meet the assumptions of the statistical model, such data are often transformed by taking logarithms. Predicted values may be required on the untransformed scale, however, and several predictors are also compared. Fairly small designs turn out to be sufficient for model fitting and for predicting. For fitting, taking replicates ensures a positive measurement error variance and smooths the predictor. This is strongly advised for standard predictors. Alternatively, we propose a predictor linear in the untransformed data, with coefficients derived from a model fitted to the logarithms of the data. It performs well on the Piazza Road data, even with no replication.

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Authors and Affiliations

  1. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

    Markus Abt & William J. Welch

  2. National Institute of Statistical Sciences, Research Triangle Park, P.O. Box 14006, North Carolina, 27709-4006

    Jerome Sacks

Authors
  1. Markus Abt
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  2. William J. Welch
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  3. Jerome Sacks
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Abt, M., Welch, W.J. & Sacks, J. Design and Analysis for Modeling and Predicting Spatial Contamination. Mathematical Geology 31, 1–22 (1999). https://doi.org/10.1023/A:1007504329298

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  • DOI: https://doi.org/10.1023/A:1007504329298

  • best linear unbiased prediction
  • dioxin contamination
  • Gaussian stochastic process
  • lognormal kriging
  • ordinary kriging
  • spatial statistics