research-article

A dynamic graph visualization perspective on eye movement data

Authors:

Michael Raschke,

Tanja Blascheck,

Daniel WeiskopfAuthors Info & Claims

ETRA '14: Proceedings of the Symposium on Eye Tracking Research and Applications

Pages 151 - 158

https://doi.org/10.1145/2578153.2578175

Published: 26 March 2014 Publication History

Abstract

During eye tracking studies, vast amounts of spatio-temporal data in the form of eye gaze trajectories are recorded. Finding insights into these time-varying data sets is a challenging task. Visualization techniques such as heat maps or gaze plots help find patterns in the data but highly aggregate the data (heat maps) or are difficult to read due to overplotting (gaze plots). In this paper, we propose transforming eye movement data into a dynamic graph data structure to explore the visualization problem from a new perspective. By aggregating gaze trajectories of participants over time periods or Areas of Interest (AOIs), a fair trade-off between aggregation and details is achieved. We show that existing dynamic graph visualizations can be used to display the transformed data and illustrate the approach by applying it to eye tracking data recorded for investigating the readability of tree diagrams.

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Cited By

Yu WJin DCai WZhao FZhang X(2022)Towards tacit knowledge mining within contextComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2022.107107226:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.cmpb.2022.107107
Burch Mten Brinke KCastella APeters GShteriyanov VVlasvinkel R(2021)Dynamic graph exploration by interactively linked node-link diagrams and matrix visualizationsVisual Computing for Industry, Biomedicine, and Art10.1186/s42492-021-00088-84:1Online publication date: 7-Sep-2021
https://doi.org/10.1186/s42492-021-00088-8
Zhao YJiang HChen QQin YXie HWu YLiu SZhou ZXia JZhou F(2021)Preserving Minority Structures in Graph SamplingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303042827:2(1698-1708)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3030428
Show More Cited By

Index Terms

A dynamic graph visualization perspective on eye movement data
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding
2. Information systems
  1. Information retrieval
    1. Document representation

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cover image ACM Conferences

ETRA '14: Proceedings of the Symposium on Eye Tracking Research and Applications

March 2014

394 pages

ISBN:9781450327510

DOI:10.1145/2578153

Conference Chairs:
Pernilla Qvarfordt
FX Palo Alto Laboratory
,
Dan Witzner Hansen
IT University, Denmark

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 March 2014

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ETRA '14

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ETRA '14: Eye Tracking Research and Applications

March 26 - 28, 2014

Florida, Safety Harbor

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Overall Acceptance Rate 69 of 137 submissions, 50%

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ETRA '25

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2025 Symposium on Eye Tracking Research and Applications

May 26 - 29, 2025

Tokyo , Japan

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Cited By

Yu WJin DCai WZhao FZhang X(2022)Towards tacit knowledge mining within contextComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2022.107107226:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.cmpb.2022.107107
Burch Mten Brinke KCastella APeters GShteriyanov VVlasvinkel R(2021)Dynamic graph exploration by interactively linked node-link diagrams and matrix visualizationsVisual Computing for Industry, Biomedicine, and Art10.1186/s42492-021-00088-84:1Online publication date: 7-Sep-2021
https://doi.org/10.1186/s42492-021-00088-8
Zhao YJiang HChen QQin YXie HWu YLiu SZhou ZXia JZhou F(2021)Preserving Minority Structures in Graph SamplingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303042827:2(1698-1708)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3030428
Burch MBrinke KCastella AKarray GPeters SShteriyanov VVlaswinkel RBurch MWestenberg MNguyen QZhao Y(2020)Guiding graph exploration by combining layouts and reorderingsProceedings of the 13th International Symposium on Visual Information Communication and Interaction10.1145/3430036.3430064(1-5)Online publication date: 8-Dec-2020
https://dl.acm.org/doi/10.1145/3430036.3430064
Muthumanickam PVrotsou KNordman AJohansson JCooper M(2018)Identification of Temporally Varying Areas of Interest in Long-Duration Eye-Tracking Data SetsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.286504225:1(87-97)Online publication date: 7-Dec-2018
https://dl.acm.org/doi/10.1109/TVCG.2018.2865042
Blascheck TSchweizer MBeck FErtl T(2017)Visual Comparison of Eye Movement PatternsComputer Graphics Forum10.1111/cgf.1317036:3(87-97)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1111/cgf.13170
Beck FBurch MDiehl SWeiskopf D(2017)A Taxonomy and Survey of Dynamic Graph VisualizationComputer Graphics Forum10.1111/cgf.1279136:1(133-159)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1111/cgf.12791
Burch MReinhardt T(2017)Dynamic Graph Visualization on Different Temporal Granularities2017 21st International Conference Information Visualisation (IV)10.1109/iV.2017.44(230-235)Online publication date: Jul-2017
https://doi.org/10.1109/iV.2017.44
Gu YWang CBixler RD'Mello S(2017)ETGraph: A graph-based approach for visual analytics of eye-tracking dataComputers & Graphics10.1016/j.cag.2016.11.00162(1-14)Online publication date: Feb-2017
https://doi.org/10.1016/j.cag.2016.11.001
Beck FBlascheck TErtl TWeiskopf D(2017)Word-Sized Eye-Tracking VisualizationsEye Tracking and Visualization10.1007/978-3-319-47024-5_7(113-128)Online publication date: 4-Feb-2017
https://doi.org/10.1007/978-3-319-47024-5_7
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