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

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. Specifically, we focus on the use of gaze data for inferring users’ identities when users are performing map-based spatial tasks. 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:

• User identification
• pedestrian navigation
• machine learning
• map reading
• individual differences
• random forest

Acknowledgments

The authors are grateful for the comments from the reviewers, which helped improve the article’s quality.

Data availability statement

The data that support the findings of this study are openly available in GitHub at https://github.com/smileliaohua/MapUserIdentificationWithEyeMovement. However, the participants were anonymized to protect the participants’ privacy.

Disclosure statement

No potential conflict of interest was reported by the author.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This research is supported by the National Natural Science Foundation of China [NSFC, Grant Nos. 42001410 and 41871366], Natural Science Foundation of Hunan Province [Grant No. 2021JJ40350], Scientific Research Project of Education Department of Hunan Province [Grant No. 19B367] and State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM [Grant No. 2021-04-03].