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 capa...

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Bibliographic Details
Published in:Japanese journal of statistics and data science 2020, Vol.3 (1), p.107-128
Main Authors: Stough, T., Cressie, N., Kang, E. L., Michalak, A. M., Sahr, K.
Format: Article
Language:English
Subjects:
Chemistry and Earth Sciences
Computer Science
Economics
Finance
Health Sciences
Humanities
Insurance
Law
Management
Mathematics and Statistics
Medicine
Original Paper
Physics
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Statistics for Business
Statistics for Engineering
Statistics for Life Sciences
Statistics for Social Sciences
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Summary: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.
ISSN:2520-8756
2520-8764
DOI:10.1007/s42081-020-00077-w