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An investigation of the impact of various geographical scales for the specification of spatial dependence

机译：调查各种地理尺度对空间依赖性规范的影响

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摘要

Ecological studies are based on characteristics of groups of individuals, which are common in various disciplines including epidemiology. It is of great interest for epidemiologists to study the geographical variation of a disease by accounting for the positive spatial dependence between neighbouring areas. However, the choice of scale of the spatial correlation requires much attention. In view of a lack of studies in this area, this study aims to investigate the impact of differing definitions of geographical scales using a multilevel model. We propose a new approach - the grid-based partitions and compare it with the popular census region approach. Unexplained geographical variation is accounted for via area-specific unstructured random effects and spatially structured random effects specified as an intrinsic conditional autoregressive process. Using grid-based modelling of random effects in contrast to the census region approach, we illustrate conditions where improvements are observed in the estimation of the linear predictor, random effects, parameters, and the identification of the distribution of residual risk and the aggregate risk in a study region. The study has found that grid-based modelling is a valuable approach for spatially sparse data while the statistical local area-based and grid-based approaches perform equally well for spatially dense data.
机译：生态学研究基于个体群体的特征，这在包括流行病学在内的各个学科中都很常见。流行病学家非常感兴趣的是，通过考虑相邻区域之间的正空间依赖性来研究疾病的地理变异。然而，空间相关性的尺度的选择需要很多注意。鉴于该领域缺乏研究，本研究旨在使用多层次模型研究不同地理尺度定义的影响。我们提出了一种新方法-基于网格的分区，并将其与流行的人口普查区域方法进行比较。无法解释的地理变化是通过特定于区域的非结构化随机效应和空间结构化随机效应来解决的，这些效应被指定为内在的条件自回归过程。与人口普查区域方法相比，使用基于网格的随机效应建模方法，我们说明了在线性预测变量，随机效应，参数的估计以及残留风险和总风险的分布识别中观察到改进的条件。研究区域。研究发现，基于网格的建模对于空间稀疏数据是一种有价值的方法，而基于统计的局部区域方法和基于网格的方法对于空间密集的数据同样具有良好的性能。

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  • 来源
    《Journal of applied statistics》 |2014年第12期|2515-2538|共24页
  • 作者

    Su Yun Kang; James McGree; Peter Baade; Kerrie Mengersen;

  • 作者单位

    Mathematical Sciences School, Queensland University of Technology, GPO Box 2434, Brisbane QLD 4001, Australia,CRC for Spatial Information, 204 Lygon Street, Carlton, VIC 3053, Australia;

    Mathematical Sciences School, Queensland University of Technology, GPO Box 2434, Brisbane QLD 4001, Australia,CRC for Spatial Information, 204 Lygon Street, Carlton, VIC 3053, Australia;

    Viertel Centre for Research in Cancer Control, Cancer Council Queensland, Gregory Tce, Fortitude Valley, Australia,School of Public Health, Queensland University of Technology, GPO Box 2434, Brisbane QLD 4001, Australia,Griffith Health Institute, Griffith University, Australia;

    Mathematical Sciences School, Queensland University of Technology, GPO Box 2434, Brisbane QLD 4001, Australia,CRC for Spatial Information, 204 Lygon Street, Carlton, VIC 3053, Australia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Bayesian hierarchical models; ecological fallacy; grid-based partitions; integrated nested Laplace approximation; intrinsic conditional autoregression; spatial epidemiology;

    机译：贝叶斯层次模型;生态谬误基于网格的分区;集成嵌套拉普拉斯近似;内在条件自回归空间流行病学;

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