his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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
Show others and affiliations
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, 2017, 64-74 p.Conference 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
2017. 64-74 p.
Keyword [en]
Cause and effect, uncertainty, evaluation, graph visualization
National Category
Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-14190DOI: 10.5220/0006102300640074ISBN: 978-989-758-228-8 (print)OAI: oai:DiVA.org:his-14190DiVA: 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 Foundation
Available from: 2017-10-02 Created: 2017-10-02 Last updated: 2017-10-12

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Bae, JuheeVentocilla, ElioRiveiro, MariaHelldin, ToveFalkman, Göran
By organisation
School of InformaticsThe Informatics Research Centre
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 23 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf