Abstract
Virtual reality (VR) headsets offer a large and immersive workspace for displaying visualizations with stereoscopic vision, as 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) system in the form of a vertical stack, showing tilted layers of geospatial data. In a formal study based on a use-case from urbanism that requires cross-referencing four layers of geospatial urban data, we compared it against more conventional systems similarly implemented in VR: a simpler grid of layers, and one map that allows for switching between layers. Performance and oculometric analyses showed a slight advantage of the two spatial-multiplexing methods (the grid or the stack) over the temporal multiplexing in blitting. Subgrouping the participants based on their preferences, characteristics, and behavior allowed a more nuanced analysis, allowing us to establish links between e.g., saccadic information, experience with video games, and preferred system. In conclusion, we found that none of the three systems are optimal and a choice of different MCV systems should be provided in order to optimally engage users.
Highlights
Analysis and decision-making in geospatial domains often rely on visualizing and understanding multiple layers of spatial data
By the metrics of our study, the stack multiple and coordinated view (MCV) system prevailed in terms of user acceptance if its visual design is taken into account, and it is mostly comparable to the grid system in terms of map layer legibility and ease of use—both leaving the only temporal multiplexing system behind
We conducted an investigation into extending the MCV concept into Virtual reality (VR), with a focus on geospatial visualization, and using them for comparisons of more than just two layers of data in a map
Summary
Analysis and decision-making in geospatial domains often rely on visualizing and understanding multiple layers of spatial data. The Semiology of graphics [1], and research on visual perception [2] led to advances in understanding how e.g., visual channels can be best employed to clearly represent as much data as possible in an effective and (often space-) efficient way [3]. While the above led to established practices for displaying many types of geospatial information, creating effective maps showing multilayered information remains a nontrivial task, even for domain experts, and research is ongoing [4]. With an ever-increasing amount of spatial data being collected and generated at faster rates and rising demand to get ahead of this data, there may not always be the time and resources to craft bespoke map visualizations for each new analysis task that requires understanding a multitude of layers. It may not be practical to display too much information on one map, no matter how well designed, when the maps are too dense or feature-rich [5]
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