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

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• 作者：Su Yun Kang ; James McGree ; Kerrie Mengersen
• 关键词：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
• 刊名：Environmental and Ecological Statistics
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：22
• 期：2
• 页码：297-327
• 全文大小：784 KB
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• 作者单位：Su Yun Kang (1) (2)
  James McGree (1) (2)
  Kerrie Mengersen (1) (2)
  
  1. Mathematical Sciences School, Queensland University of Technology, GPO Box 2434, Brisbane, QLD, 4001, Australia
  2. CRC for Spatial Information, 204 Lygon Street, Carlton, VIC, 3053, Australia
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Life Sciences
  Ecology
  Statistics
  Mathematical Biology
  Evolutionary Biology
  
• 出版者：Springer Netherlands
• ISSN：1573-3009

文摘

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.