Abstract
In this article, we describe a new feature for exploring eye movement data based on image-based clustering. To reach this goal, visual attention is taken into account to compute a list of thumbnail images from the presented stimulus. These thumbnails carry information about visual scanning strategies, but showing them just in a space-filling and unordered fashion does not support the detection of patterns over space, time, or study participants. In this article, we present an enhancement of the EyeCloud approach that is based on standard word cloud layouts adapted to image thumbnails by exploiting image information to cluster and group the thumbnails that are visually attended.To also indicate the temporal sequence of the thumbnails, we add color-coded links and further visual features to dig deeper in the visual attention data. The usefulness of the technique is illustrated by applying it to eye movement data from a formerly conducted eye tracking experiment investigating route finding tasks in public transport maps. Finally, we discuss limitations and scalability issues of the approach.Graphic abstract
Highlights
Exploring eye movement data (Duchowski 2003; Holmqvist et al 2011) is pretty challenging due to its spatio-temporal nature (Blascheck et al 2015)
In this article, we describe a new feature for exploring eye movement data based on image-based clustering
We present an enhancement of the EyeCloud approach that is based on standard word cloud layouts adapted to image thumbnails by exploiting image information to cluster and group the thumbnails that are visually attended
Summary
Exploring eye movement data (Duchowski 2003; Holmqvist et al 2011) is pretty challenging due to its spatio-temporal nature (Blascheck et al 2015). Interactive visualizations of eye movement data (Blascheck et al 2017) can support data analysts to detect normal or abnormal patterns, but each visualization is just designed for a certain task at hand (Ware 2008). Detecting patterns over space, time, and the study participants as well as providing detailed and scalable information about the presented stimulus is a difficult task. Algorithmic preprocesses of the data and visual encodings of the results can interplay to provide the best possible ways to interpret the data, to build, confirm, reject, or refine hypotheses (Keim 2012), and derive knowledge from the recorded eye movement data. This article presents an extended version of the EyeCloud technique (Burch et al 2019c) by adding, as major ideas among others, an image-based clustering concept as well as the temporal information to the original word cloud-based visualizations.
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