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A Multiscale Spatially Varying Coefficient Model for Regional Analysis of Topsoil Geochemistry

Author

Listed:
  • Keunseo Kim

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Hyojoong Kim

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Vinnam Kim

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Heeyoung Kim

    (Korea Advanced Institute of Science and Technology (KAIST))

Abstract

A motivating example for this paper is to study a topsoil geochemical process across a large region. In regional environmental health studies, ambient levels of toxic substances in topsoil are commonly used as surrogates for personal exposure to toxic substances. However, toxicity levels in topsoil are usually sparsely measured at a limited number of point locations. Consequently, topsoil measurements only provide highly localized regional information and cannot be representative of the surrounding area. Instead, it is standard practice to use point-referenced measurements of stream sediments, because they are widely available across a region and are correlated with topsoil measurements at nearby locations. For more effective regional modeling of topsoil geochemistry, we develop a spatially varying coefficient model that integrates point-level topsoil and point-referenced area-level stream sediment data. The proposed model incorporates two spatial characteristics: the local spatial autocorrelation in the latent topsoil process and the spatially varying relationship between the latent topsoil and stream sediment processes. The former is modeled indirectly via a conditional autoregressive model for the stream sediment process, and the latter is modeled by spatially varying coefficients that follow a multivariate Gaussian process. We apply the proposed model to a real dataset of arsenic concentration and demonstrate better performance than competing models.

Suggested Citation

  • Keunseo Kim & Hyojoong Kim & Vinnam Kim & Heeyoung Kim, 2020. "A Multiscale Spatially Varying Coefficient Model for Regional Analysis of Topsoil Geochemistry," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(1), pages 74-89, March.
    • Handle: RePEc:spr:jagbes:v:25:y:2020:i:1:d:10.1007_s13253-019-00379-x
    DOI: 10.1007/s13253-019-00379-x
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    References listed on IDEAS

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    1. C. A. Calder & P. F. Craigmile & J. Zhang, 2009. "Regional Spatial Modeling of Topsoil Geochemistry," Biometrics, The International Biometric Society, vol. 65(1), pages 206-215, March.
    2. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Calder, Catherine A. & Holloman, Christopher H. & Bortnick, Steven M. & Strauss, Warren & Morara, Michele, 2008. "Relating Ambient Particulate Matter Concentration Levels to Mortality Using an Exposure Simulator," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 137-148, March.
    5. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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