Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package
Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package
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DOI：
10.18637/jss.v084.i09
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发表时间：
2018-04-01
影响因子：
5.8
通讯作者：
Napier, Gary
中科院分区：
文献类型：
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作者：
Lee, Duncan;Rushworth, Alastair;Napier, Gary
与非重叠面积单位有关的空间数据在经济学、环境科学、流行病学和社会科学等领域很普遍，为分析这些数据开发了一整套建模工具。许多利用条件自回归（CAR）先验来捕获这些数据中固有的空间自相关性，并且已经开发了诸如CARBayes和R-INLA之类的软件包，以使其他人可以轻松地访问这些模型。这样的空间数据通常可用于多个时间段，并且用于捕获随时间变化的空间动态的方法的开发是当前许多研究的焦点。相当大一部分的文献都集中在扩展CAR先验的时空域，本文提出了R包CARBayesST，这是第一个专用的软件包的时空面积单位建模与条件自回归先验。该软件包可以拟合一系列侧重于时空建模不同方面的模型，包括估计总体空间和时间趋势，以及确定显示出升高值的面积单位群。本文概述了类的模型，软件包实现，然后将它们应用到模拟和两个真实的例子，从流行病学和住房市场分析领域。
Spatial data relating to non-overlapping areal units are prevalent in fields such as economics, environmental science, epidemiology and social science, and a large suite of modeling tools have been developed for analysing these data. Many utilize conditional autoregressive (CAR) priors to capture the spatial autocorrelation inherent in these data, and software packages such as CARBayes and R-INLA have been developed to make these models easily accessible to others. Such spatial data are typically available for multiple time periods, and the development of methodology for capturing temporally changing spatial dynamics is the focus of much current research. A sizeable proportion of this literature has focused on extending CAR priors to the spatio-temporal domain, and this article presents the R package CARBayesST, which is the first dedicated software package for spatio-temporal areal unit modeling with conditional autoregressive priors. The software package allows to fit a range of models focused on different aspects of space-time modeling, including estimation of overall space and time trends, and the identification of clusters of areal units that exhibit elevated values. This paper outlines the class of models that the software package implement, before applying them to simulated and two real examples from the fields of epidemiology and housing market analysis.