Abstract data has no natural scale and so interactive data visualizations must provide techniques to allow the user to choose their viewpoint and scale. Such techniques are well established in desktop visualization tools. The two most common techniques are zoom+pan and overview+detail. However, how best to enable the analyst to navigate and view abstract data at different levels of scale in immersive environments has not previously been studied. We report the findings of the first systematic study of immersive navigation techniques for 3D scatterplots. We tested four conditions that represent our best attempt to adapt standard 2D navigation techniques to data visualization in an immersive environment while still providing standard immersive navigation techniques through physical movement and teleportation. We compared room-sized visualization versus a zooming interface, each with and without an overview. We find significant differences in participants' response times and accuracy for a number of standard visual analysis tasks. Both zoom and overview provide benefits over standard locomotion support alone (i.e., physical movement and pointer teleportation). However, which variation is superior, depends on the task. We obtain a more nuanced understanding of the results by analyzing them in terms of a time-cost model for the different components of navigation: way-finding, travel, number of travel steps, and context switching.
|Original language||English (US)|
|Number of pages||1|
|Journal||IEEE Transactions on Visualization and Computer Graphics|
|State||Published - 2020|
Bibliographical noteKAUST Repository Item: Exported on 2020-12-22
Acknowledged KAUST grant number(s): OSR-2015-CCF-2533-01
Acknowledgements: This research was supported in part under KAUST Office of Sponsored Research (OSR) award OSR-2015-CCF-2533-01 and Australian Research Council’s Discovery Projects funding scheme DP180100755. Yalong Yang was supported by a Harvard Physical Sciences and Engineering Accelerator Award. We also wish to thank all our participants for their time and our reviewers for their comments and feedback.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.