Spatial analysis and visualization of global data on multi-resolution hexagonal grids

In this article, computation for the purpose of spatial visualization is presented in the context of understanding the variability in global environmental processes. Here, we generate synthetic but realistic global data sets and input them into computational algorithms that have a visualization capability; we call this a simulation–visualization system. Visual- ization is key here because the algorithms we are evaluating must respect the spatial structure of the input. We modify, augment, and integrate four existing component technologies: sta- tistical conditional simulation, Discrete Global Grids (DGGs), Array Set Addressing, and a visualization platform for displaying our results on a globe. The internal representation of the data to be visualized is built around the need for efficient storage and computation as well as the need to move up and down resolutions in a mutually consistent way. In effect, we have constructed a Geographic Information System (GIS) that is based on a DGG and has desirable data storage, computation, and visualization capabilities. We provide an exam- ple of how our simulation–visualization system may be used, by evaluating a computational algorithm called Spatial Statistical Data Fusion (SSDF) that was developed for use on big, remote sensing data sets.本文围绕理解全球环境过程的变异性这一研究背景，介绍了服务于空间可视化的计算方法。在此研究中，我们生成合成但贴近真实的全球数据集，并将其输入具备可视化能力的计算算法，我们将这一系统称为模拟-可视化系统。可视化在此处是核心环节，因为我们所评估的算法必须尊重输入数据的空间结构。我们对四项现有组件技术进行修改、扩充与整合：统计条件模拟、离散全球格网（Discrete Global Grids, DGGs）、数组集寻址（Array Set Addressing），以及用于在全球场景中展示结果的可视化平台。待可视化数据的内部表示，围绕高效存储与计算的需求，以及以相互一致的方式进行分辨率升降级的需求构建。实际上，我们构建了一套基于离散全球格网的地理信息系统（Geographic Information System, GIS），其具备优异的数据存储、计算与可视化能力。我们通过评估一款专为大规模遥感数据集开发的计算算法——空间统计数据融合（Spatial Statistical Data Fusion, SSDF），展示了本模拟-可视化系统的应用方式。