Published in

Copernicus Publications, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (XLIII-B4-2020), p. 623-630, 2020

DOI: 10.5194/isprs-archives-xliii-b4-2020-623-2020



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Visualizing Multilayered Geospatial Data in Virtual Reality to Assess Public Lighting

Journal article published in 2020 by Maxim Spur, Nicolas Houel, Vincent Tourre ORCID
This paper is available in a repository.
This paper is available in a repository.

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Abstract. With the improvement and proliferation of virtual reality devices, their use for research and professional activity is broadening, fostering the advent of the field of immersive analytics, as is their acceptance among consumers. Other than the heightened sense of immersion into visualized data they provide, they also make displays of much larger apparent size and different positioning practical than what would be possible otherwise. Drawing on these benefits, we implemented a development of Multiple and Coordinated Displays (MCVs) for geovisualization that stacks different layers of data above each other, tilted for legibility. In a formal experiment, we evaluated it and two other, comparable MCV methods implemented in VR for their usefulness in analyzing public perception and soliciting public feedback regarding urban street lighting. In that field, the direction has recently been shifting from purely systemic development to a participatory approach, thus our investigation was into how a system like this could facilitate participation that can yield actionable results. Previous analysis of interaction data and usability questionnaires reveals preferences for certain systems depending on user characteristics, with the stack system showing a slight advantage over a grid of layers and especially over temporal multiplexing. We show that regardless of MCV variation, participants were able to analyze and provide feedback on public lighting situations that can directly contribute to urbanist work. The MCV approach further aided in understanding their choices, as eye-tracking allowed us to analyze attention to individual data layers.