As spatial datasets are becoming increasingly large and unwieldy, exact inference on spatial models becomes computationally prohibitive. Various approximation methods have been proposed to reduce the computational burden. Although comprehensive reviews on these approximation methods exist, comparisons of their performances are limited to small and medium sizes of datasets for a few selected methods. To achieve a comprehensive comparison comprising as many methods as possible, we organized the Competition on Spatial Statistics for Large Datasets. This competition had the following novel features: (1) we generated synthetic datasets with the ExaGeoStat software so that the number of generated realizations ranged from 100 thousand to 1 million; (2) we systematically designed the data-generating models to represent spatial processes with a wide range of statistical properties for both Gaussian and non-Gaussian cases; (3) the competition tasks included both estimation and prediction, and the results were assessed by multiple criteria; and (4) we have made all the datasets and competition results publicly available to serve as a benchmark for other approximation methods. In this paper, we disclose all the competition details and results along with some analysis of the competition outcomes.

随着空间数据集变得越来越大和笨拙，对空间模型的精确推断在计算上变得令人望而却步。已经提出了各种近似方法来减少计算负担。尽管存在对这些近似方法的全面评论，但它们的性能比较仅限于少数选定方法的中小型数据集。为了实现包含尽可能多的方法的全面比较，我们组织了大数据集空间统计竞赛。本次比赛具有以下新特点：（1）我们使用ExaGeoStat生成了合成数据集软件使生成的实现数量从 10 万到 100 万不等；(2) 我们系统地设计了数据生成模型来表示具有广泛统计特性的高斯和非高斯情况的空间过程；（3）竞赛任务包括估计和预测，结果采用多标准评定；(4) 我们公开了所有数据集和比赛结果，作为其他近似方法的基准。在本文中，我们公开了所有的比赛细节和结果以及对比赛结果的一些分析。