Open AccessSpatial analysis and visualization of global data on multi-resolution hexagonal grids
Author(s) -
T. Stough,
Noel Cressie,
Emily L. Kang,
A. M. Michalak,
Kevin Sahr
Publication year - 2020
Publication title -
japanese journal of statistics and data science/japanese journal of statistics and data science
Language(s) - English
Resource type - Journals
eISSN - 2520-8764
pISSN - 2520-8756
DOI - 10.1007/s42081-020-00077-w
Subject(s) - visualization , computer science , data mining , computation , context (archaeology) , data visualization , representation (politics) , spatial analysis , big data , theoretical computer science , computational science , algorithm , remote sensing , geography , archaeology , politics , political science , law
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. Visualization is key here, because the algorithms which we are evaluating must respect the spatial structure of the input. We modify, augment, and integrate four existing component technologies: statistical 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 downresolutions in a mutually consistent way. In effect, we have constructed a Geographic Information System that is based on a DGG and has desirable data storage, computation, and visualization capabilities. We provide an example of how our simulation–visualization system may be used, by evaluating a computational algorithm called Spatial Statistical Data Fusion that was developed for use on big, remote-sensing data sets.