MapStack: Exploring Multilayered Geospatial Data in Virtual Reality

Maxim Spur, Vincent Tourre, Erwan David, Guillaume Moreau, Patrick Callet

Abstract

Virtual reality (VR) headsets offer a large and immersive workspace for displaying visualizations with stereoscopic vision, compared to traditional environments with monitors or printouts. The controllers for these devices further allow direct three-dimensional interaction with the virtual environment. In this paper, we make use of these advantages to implement a novel multiple and coordinated view (MCV) in the form of a vertical stack, showing tilted layers of geospatial data to facilitate an understanding of multi-layered maps. A formal study based on a use-case from urbanism that requires cross-referencing four layers of geospatial urban data augments our arguments for it by comparing it to more conventional systems similarly implemented in VR: a simpler grid of layers, and switching (blitting) layers on one map. Performance and oculometric analyses showed an advantage of the two spatial-multiplexing methods (the grid or the stack) over the temporal multiplexing in blitting. Overall, users tended to prefer the stack, be ambivalent to the grid, and show dislike for the blitting map. Perhaps more interestingly, we were also able to associate preferences in systems with user characteristics and behavior.

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Paper Citation


in Harvard Style

Spur M., Tourre V., David E., Moreau G. and Callet P. (2020). MapStack: Exploring Multilayered Geospatial Data in Virtual Reality.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, ISBN 978-989-758-402-2, pages 88-99. DOI: 10.5220/0008978600880099


in Bibtex Style

@conference{ivapp20,
author={Maxim Spur and Vincent Tourre and Erwan David and Guillaume Moreau and Patrick Callet},
title={MapStack: Exploring Multilayered Geospatial Data in Virtual Reality},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,},
year={2020},
pages={88-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008978600880099},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,
TI - MapStack: Exploring Multilayered Geospatial Data in Virtual Reality
SN - 978-989-758-402-2
AU - Spur M.
AU - Tourre V.
AU - David E.
AU - Moreau G.
AU - Callet P.
PY - 2020
SP - 88
EP - 99
DO - 10.5220/0008978600880099