The most prevalent spatial data setting is, arguably, that of so-called geostatistical data, data that arise as random variables observed at fixed spatial locations. Collection of such data in space and in time has grown enormously in the past two decades. With it has grown a substantial array of methods to analyze such data. Here, we attempt a review of a fully model-based perspective for such data analysis, the approach of hierarchical modeling fitted within a Bayesian framework. The benefit, as with hierarchical Bayesian modeling in general, is full and exact inference, with proper assessment of uncertainty. Geostatistical modeling includes univariate and multivariate data collection at sites, continuous and categorical data at sites, static and dynamic data at sites, and datasets over very large numbers of sites and long periods of time. Within the hierarchical modeling framework, we offer a review of the current state of the art in these settings.

中文翻译：

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地统计数据的贝叶斯建模与分析。

最普遍的空间数据设置可以说是所谓的地统计数据，即在固定空间位置观察到的作为随机变量的数据。在过去的二十年中，此类数据在空间和时间上的收集已大大增加。随着它的发展，已经有大量分析这类数据的方法。在这里，我们尝试对此类数据分析的完全基于模型的观点进行回顾，这是一种适合贝叶斯框架的分层建模方法。通常，与分层贝叶斯建模一样，这样做的好处是可以进行充分而准确的推断，并可以对不确定性进行适当的评估。地统计建模包括站点上的单变量和多变量数据收集，站点上的连续和分类数据，站点上的静态和动态数据以及大量站点和长时间内的数据集。