#5158. Identifying map users with eye movement data from map-based spatial tasks: user privacy concerns

July 2026publication date
Proposal available till 23-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Geography, Planning and Development;
Civil and Structural Engineering;
Management of Technology and Innovation;
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Abstract:
Individuals with different characteristics exhibit different eye movement patterns in map reading and wayfinding tasks. In this study, we aim to explore whether and to what extent map users’ eye movements can be used to detect who created them. We collected 32 participants’ eye movement data as they utilized maps to complete a series of self-localization and spatial orientation tasks. We extracted five sets of eye movement features and trained a random forest classifier. We used a leave-one-task-out approach to cross-validate the classifier and achieved the best identification rate of 89%, with a 2.7% equal error rate. This result is among the best performances reported in eye movement user identification studies. We evaluated the feature importance and found that basic statistical features (e.g. pupil size, saccade latency and fixation dispersion) yielded better performance than other feature sets (e.g. spatial fixation densities, saccade directions and saccade encodings). The results open the potential to develop personalized and adaptive gaze-based map interactions but also raise concerns about user privacy protection in data sharing and gaze-based geoapplications.
Keywords:
Individual differences; machine learning; map reading; pedestrian navigation; random forest; User identification

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