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Visualizing spatiotemporal models with virtual reality: from fully immersive environments to applications in stereoscopic view

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  • Stefano Castruccio
  • Marc G. Genton
  • Ying Sun

Abstract

Recent advances in computing hardware and software present an unprecedented opportunity for statisticians who work with data indexed in space and time to visualize, explore and assess the structure of the data and to improve resulting statistical models. We present results of a 3‐year collaboration with a team of visualization experts on the use of stereoscopic view and virtual reality (VR) to visualize spatiotemporal data with animations on non‐trivial manifolds. We first present our experience with fully immersive VR with motion tracking devices that enable users to explore global three‐dimensional time–temperature fields on a spherical shell interactively. We then introduce a suite of applications with VR mode, freely available for smartphones, to port a visualization experience to any interested people. We also discuss recent work with head‐mounted devices such as a VR headset with motion tracking sensors.

Suggested Citation

  • Stefano Castruccio & Marc G. Genton & Ying Sun, 2019. "Visualizing spatiotemporal models with virtual reality: from fully immersive environments to applications in stereoscopic view," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 379-387, February.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:2:p:379-387
    DOI: 10.1111/rssa.12381
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    Cited by:

    1. Marc G. Genton & Ying Sun, 2019. "Comments on: Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 338-341, June.
    2. Felipe Tagle & Marc G. Genton & Andrew Yip & Suleiman Mostamandi & Georgiy Stenchikov & Stefano Castruccio, 2020. "Rejoinder to the discussion on A high‐resolution bilevel skew‐t stochastic generator for assessing Saudi Arabia's wind energy resources," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    3. Huang Huang & Stefano Castruccio & Allison H. Baker & Marc G. Genton, 2023. "Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 324-344, June.
    4. Matthew Edwards & Stefano Castruccio & Dorit Hammerling, 2019. "A Multivariate Global Spatiotemporal Stochastic Generator for Climate Ensembles," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 464-483, September.

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