Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Kang, Su Yun, McGree, James, & Mengersen, Kerrie (2015) Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches. Environmental and Ecological Statistics, 2(2), pp. 297-327.

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Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.

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ID Code: 75781
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Gamma moving average model, grid-based spatial data, integrated nested Laplace approximation, log Gaussian Cox process, Markov chain Monte Carlo, semiparametric adaptive Gaussian Markov random field model
DOI: 10.1007/s10651-014-0299-y
ISSN: 1573-3009
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2014 Springer
Deposited On: 28 Aug 2014 22:49
Last Modified: 06 Jul 2016 11:41

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