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Evaluating Multi-Attributes on Cause and Effect Relationship Visualization
Högskolan i Skövde, Institutionen för informationsteknologi.
Högskolan i Skövde, Institutionen för informationsteknologi.
Högskolan i Skövde, Institutionen för informationsteknologi.ORCID-id: 0000-0003-2900-9335
Högskolan i Skövde, Institutionen för informationsteknologi.ORCID-id: 0000-0001-6245-5850
Vise andre og tillknytning
2017 (engelsk)Inngår i: 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, s. 64-74Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
SciTePress , 2017. s. 64-74
Emneord [en]
Cause and effect, uncertainty, evaluation, graph visualization
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifikatorer
URN: urn:nbn:se:hj:diva-43243DOI: 10.5220/0006102300640074ISI: 000444939500005Scopus ID: 2-s2.0-85040593124Lokal ID: 0;0;miljJAILISBN: 978-989-758-228-8 (tryckt)OAI: oai:DiVA.org:hj-43243DiVA, id: diva2:1293748
Konferanse
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
Forskningsfinansiär
Knowledge FoundationTilgjengelig fra: 2019-03-05 Laget: 2019-03-05 Sist oppdatert: 2019-08-23bibliografisk kontrollert

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