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Evaluating Multi-Attributes on Cause and Effect Relationship Visualization
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2900-9335
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-6245-5850
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2017 (English)In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017): Volumne 3: IVAPP / [ed] Alexandru Telea, Jose Braz, Lars Linsen, SciTePress, 2017, p. 64-74Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents findings about visual representations of cause and effect relationship's direction, strength, and uncertainty based on an online user study. While previous researches focus on accuracy and few attributes, our empirical user study examines accuracy and the subjective ratings on three different attributes of a cause and effect relationship edge. The cause and effect direction was depicted by arrows and tapered lines; causal strength by hue, width, and a numeric value; and certainty by granularity, brightness, fuzziness, and a numeric value. Our findings point out that both arrows and tapered cues work well to represent causal direction. Depictions with width showed higher conjunct accuracy and were more preferred than that with hue. Depictions with brightness and fuzziness showed higher accuracy and were marked more understandable than granularity. In general, depictions with hue and granularity performed less accurately and were not preferred compared to the ones with numbers or with width and brightness.

Place, publisher, year, edition, pages
SciTePress, 2017. p. 64-74
Keywords [en]
Cause and effect, uncertainty, evaluation, graph visualization
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
URN: urn:nbn:se:his:diva-14190DOI: 10.5220/0006102300640074ISBN: 978-989-758-228-8 (print)OAI: oai:DiVA.org:his-14190DiVA, id: diva2:1146273
Conference
8th International Conference on Information Visualization Theory and Applications (IVAPP), part of the 12th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), February 27-March 1, 2017, in Porto, Portugal
Funder
Knowledge FoundationAvailable from: 2017-10-02 Created: 2017-10-02 Last updated: 2018-06-11Bibliographically approved

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Bae, JuheeVentocilla, ElioRiveiro, MariaHelldin, ToveFalkman, Göran

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