Evaluating the Usability of a Gaze-Adaptive Approach for Identifying and Comparing Raster Values between Multilayers

Bibliographic Data

ID22033952
AuthorsChangbo Zhang (0000-0003-4621-2183, Hunan Normal University), H Liao (0000-0002-6304-329X, Hunan Normal University, corresponding author), Yongbo Huang (0009-0005-6754-6176, South China Normal University), Weihua Dong (0000-0001-6097-7946, Beijing Normal University)
Year2023
Volume12
Issue10
Pages412
Publication date2023-10-08
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi12100412
OpenAlexW4387458921
LanguageEN
Citations received1
References cited48

Raster maps provide intuitive visualizations of remote sensing data representing various phenomena on the Earth’s surface. Reading raster maps with intricate information requires a high cognitive workload, especially when it is necessary to identify and compare values between multiple layers. In traditional methods, users need to repeatedly move their mouse and switch their visual focus between the map content and legend to interpret various grid value meanings. Such methods are ineffective and may lead to the loss of visual context for users. In this research, we aim to explore the potential benefits and drawbacks of gaze-adaptive interactions when interpreting raster maps. We focus on the usability of the use of low-cost eye trackers on gaze-based interactions. We designed two gaze-adaptive methods, gaze fixed and gaze dynamic adaptations, for identifying and comparing raster values between multilayers. In both methods, the grid content of different layers is adaptively adjusted depending on the user’s visual focus. We then conducted a user experiment by comparing such adaptation methods with a mouse dynamic adaptation method and a traditional method. Thirty-one participants (n = 31) were asked to complete a series of single-layer identification and multilayer comparison tasks. The results indicated that although gaze interaction with adaptive legends confused participants in single-layer identification, it improved multilayer comparison efficiency and effectiveness. The gaze-adaptive approach was well received by the participants overall, but was also perceived to be distracting and insensitive. By analyzing the participants’ eye movement data, we found that different methods exhibited significant differences in visual behaviors. The results are helpful for gaze-driven adaptation research in (geo)visualization in the future

BitTorrent tracker · Computer vision · Eye tracking · Gaze · Human–computer interaction · Legibility · Raster graphics · Usability · Visualization · Computer Science · Data Visualization and Analytics · Gaze Tracking and Assistive Technology · Psychology · Spatial Cognition and Navigation · Artificial Intelligence

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