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Open access
Date
2018-08-28Type
- Conference Paper
ETH Bibliography
yes
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Abstract
The reading behavior on maps can strongly vary with factors such as background knowledge, mental model, task or the visual design of a map. Therefore, in cartography, eye tracking experiments have a long tradition to foster the visual attention. In this work-in-progress, we use an unsupervised machine learning pipeline for clustering eye tracking data. In particular, we focus on methods that help to validate and evaluate the clustering results since this is a common issue of unsupervised machine learning. First results indicate that validation using the silhouette score alone is a poor choice and should, for example, be accompanied by a visual validation using t-distributed stochastic neighbor embedding (t-SNE). Show more
Permanent link
https://doi.org/10.3929/ethz-b-000290476Publication status
publishedBook title
Spatial Big Data and Machine Learning in GIScience, Workshop at GIScience 2018Pages / Article No.
Publisher
Spatial Big DataEvent
Subject
eye tracking; unsupervised machine learning; clustering; map taskOrganisational unit
03901 - Raubal, Martin / Raubal, Martin
Funding
162886 - Intention-Aware Gaze-Based Assistance on Maps (SNF)
Related publications and datasets
Is cited by: https://doi.org/10.3929/ethz-b-000513243
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ETH Bibliography
yes
Altmetrics