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. 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.

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The authors would like to thank Abhishek Chatterjee for processing and providing inputs on the use of PCTM/GEOS-4 global model data. They would also like to thank Jonathan Bradley, Jonathan Hobbs, Vineet Yadav, Chun-Houh Chen, Wolfgang Härdle, Antony Unwin, and Han-Ming (Hank) Wu for their contributions and comments. The work described in this article was carried out in part by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. It was supported by NASA’s Earth Science Technology Office through its Advanced Information Systems Technology program. Cressie’s research was partially supported by an Australian Research Council Discovery Project DP190100180. Kang’s research was partially supported by the Simons Foundation’s Collaboration Award (#317298) and the Taft Research Center at the University of Cincinnati.

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Correspondence to T. Stough.

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Stough, T., Cressie, N., Kang, E.L. et al. Spatial analysis and visualization of global data on multi-resolution hexagonal grids. Jpn J Stat Data Sci 3, 107–128 (2020).

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  • Geographic Information Science
  • Discrete global grids
  • Raster data modelling
  • Spatial analysis
  • Remote sensing