Areal and point source spatial data models

Researchers using spatial data are often faced with a mix of data obtained from several levels of scale, aggregation and point reference data. Classical geospatial regressions do not deal with this mix very well, and standard ordinary regressions even worst. A unified treatment is the topic of a recent article, “Reparameterized and Marginalized Posterior and Predictive Sampling for Complex Bayesian Geostatistical Models” in Volume 18, Number 2 of JCGS. In short, the authors cleverly reparameterized and recast the problem so as to allow efficient MCMC samplers to address the Bayesian estimation task. Their article’s supplemental materials provide the R and OpenBugs codes to address the efficient estimation tasks outlined.

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