FeaturEyeTrack: automatic matching of eye tracking data with map features on interactive maps
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FeaturEyeTrack: automatic matching of eye tracking data with map features on interactive maps ¨ 1 · Peter Kiefer1 · Martin Raubal1 Fabian Gobel Received: 13 July 2018 / Revised: 19 December 2018 / Accepted: 22 February 2019 / © Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract Map reading is a visual task that can strongly vary between individuals and maps of different characteristics. Aspects such as where, when, how long, and in which sequence information on a map is looked at can reveal valuable insights for both the map design process and to better understand cognitive processes of the map user. Contrary to static maps, for which many eye tracking studies are reported in the literature, established methods for tracking and analyzing visual attention on interactive maps are yet missing. In this paper, we present a framework called FeaturEyeTrack that allows to automatically log the cartographic features that have been inspected as well as the mouse input during the interaction with digital interactive maps. In particular, the novelty of FeaturEyeTrack lies in matching of gaze with the vector model of the current map visualization, therefore enabling a very detailed analysis without the requirement for manual annotation. Furthermore, we demonstrate the benefits of this approach in terms of manual work, level of detail and validity compared to state-of-the-art methods through a case study on an interactive cartographic web map. Keywords Eye tracking · Eye movement analysis · Interactive maps · User logging · Human computer interaction
1 Motivation Map reading is a visual task that can strongly vary between individuals and maps of different characteristics. Aspects such as where, when, how long, and in which sequence certain information on a map is looked at can lead to valuable insights for the map design process and contribute to a better understanding of the map user [67]. Visual attention is a complex Fabian G¨obel
[email protected] Peter Kiefer [email protected] Martin Raubal [email protected] 1
ETH Zurich, Institute of Cartography and Geoinformation, CH-8093 Zurich, Switzerland
Geoinformatica
interplay between top-down and bottom-up processes. This means, it is guided by intentions and plans [44] as well as by visual cues in the scene [5]. Thus visual attention reflects both, aspects of the user, such as background knowledge, expectations and mental model, level of expertise and the current task as well as the aspects of the map design, such as visual clutter or saliency of map features. Understanding these aspects is very important for the optimization of a map design and can help to make map reading more effective and efficient [48, 51]. Not surprisingly, logging and analyzing visual attention on maps has been part of cartographic methodology for quite some time (see [41], for an overview). While early studies tested static cartographic maps [69], nowadays, due to the wide availability of online cartography and web mapping services, such as Google Maps (http://maps.google.com)
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