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2013 Spatial models for point and areal data using Markov random fields on a fine grid
Christopher J. Paciorek
Electron. J. Statist. 7: 946-972 (2013). DOI: 10.1214/13-EJS791

Abstract

I consider the use of Markov random fields (MRFs) on a fine grid to represent latent spatial processes when modeling point-level and areal data, including situations with spatial misalignment. Point observations are related to the grid cell in which they reside, while areal observations are related to the (approximate) integral over the latent process within the area of interest. I review several approaches to specifying the neighborhood structure for constructing the MRF precision matrix, presenting results comparing these MRF representations analytically, in simulations, and in two examples. The results provide practical guidance for choosing a spatial process representation and highlight the importance of this choice. In particular, the results demonstrate that, and explain why, standard CAR models can behave strangely for point-level data. They show that various neighborhood weighting approaches based on higher-order neighbors that have been suggested for MRF models do not produce smooth fields, which raises doubts about their utility. Finally, they indicate that an MRF that approximates a thin plate spline compares favorably to standard CAR models and to kriging under many circumstances.

Citation

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Christopher J. Paciorek. "Spatial models for point and areal data using Markov random fields on a fine grid." Electron. J. Statist. 7 946 - 972, 2013. https://doi.org/10.1214/13-EJS791

Information

Received: 1 May 2012; Published: 2013
First available in Project Euclid: 15 April 2013

zbMATH: 1337.62302
MathSciNet: MR3044505
Digital Object Identifier: 10.1214/13-EJS791

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

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