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  • Moragaetal2017

    Rights statement: This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 21, A, 2018 DOI: 10.1016/j.spasta.2017.04.006

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A geostatistical model for combined analysis of point-level and area-level data using INLA and SPDE

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<mark>Journal publication date</mark>08/2017
<mark>Journal</mark>Spatial Statistics
Issue numberA
Volume21
Number of pages15
Pages (from-to)27-41
Publication StatusPublished
Early online date1/06/17
<mark>Original language</mark>English

Abstract

In this paper a Bayesian geostatistical model is presented for fusion of data obtained at point and areal resolutions. The model is fitted using the INLA and SPDE approaches. In the SPDE approach, a continuously indexed Gaussian random field is represented as a discretely indexed Gaussian Markov random field (GMRF) by means of a finite basis function defined on a triangulation of the region of study. In order to allow the combination of point and areal data, a new projection matrix for mapping the GMRF from the observation locations to the triangulation nodes is proposed which takes into account the types of data to be combined. The performance of the model is examined and compared with the performance of the method RAMPS via simulation when it is fitted to (i) point, (ii) areal, and (iii) point and areal data to predict several simulated surfaces that can appear in real settings. The model is applied to predict the concentration of fine particulate matter (PM2.5), in Los Angeles and Ventura counties, United States, during 2011.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 21, A, 2018 DOI: 10.1016/j.spasta.2017.04.006