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

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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.
Publisher
SPRINGER
Issue Date
2020-03
Language
English
Article Type
Article
Citation

JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, v.25, no.1, pp.74 - 89

ISSN
1085-7117
DOI
10.1007/s13253-019-00379-x
URI
http://hdl.handle.net/10203/272358
Appears in Collection
IE-Journal Papers(저널논문)
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