The Gaussian process is an indispensable tool for spatial data analysts. The onset of the “big data” era, however, has lead to the traditional Gaussian process being computationally infeasible for modern spatial data. As such, various alternatives to the full Gaussian process that are more amenable to handling big spatial data have been proposed. These modern methods often exploit low-rank structures and/or multi-core and multi-threaded computing environments to facilitate computation. This study provides, first, an introductory overview of several methods for analyzing large spatial data. Second, this study describes the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology. Specifically, each research group was provided with two training datasets (one simulated and one observed) along with a set of prediction locations. Each group then wrote their own implementation of their method to produce predictions at the given location and each was subsequently run on a common computing environment. The methods were then compared in terms of various predictive diagnostics. Supplementary materials regarding implementation details of the methods and code are available for this article online.

中文翻译：

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大空间数据分析方法的案例研究竞赛

高斯过程是空间数据分析师不可或缺的工具。然而，“大数据”时代的到来导致传统的高斯过程对于现代空间数据在计算上不可行。因此，已经提出了更适合处理大空间数据的完整高斯过程的各种替代方案。这些现代方法通常利用低阶结构和/或多核和多线程计算环境来促进计算。本研究首先介绍了分析大型空间数据的几种方法。其次，本研究描述了由在方法论方面具有丰富专业知识的不同团体实施的所描述方法之间的预测竞争结果。具体来说，为每个研究小组提供了两个训练数据集（一个是模拟的，一个是观察的）以及一组预测位置。然后，每个小组编写自己的方法实现，以在给定位置生成预测，然后每个小组都在公共计算环境上运行。然后根据各种预测诊断对这些方法进行比较。有关方法和代码的实现细节的补充材料可在线获取本文。