Evaluating Multi-attributes on Cause and Effect Relationship Visualization

Juhee Bae, Elio Ventocilla, Maria Riveiro, Tove Helldin, Göran Falkman

2017

Abstract

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.

Paper Citation

in Harvard Style

Bae J., Ventocilla E., Riveiro M., Helldin T. and Falkman G. (2017). Evaluating Multi-attributes on Cause and Effect Relationship Visualization . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017) ISBN 978-989-758-228-8, pages 64-74. DOI: 10.5220/0006102300640074

in Bibtex Style

@conference{ivapp17,
author={Juhee Bae and Elio Ventocilla and Maria Riveiro and Tove Helldin and Göran Falkman},
title={Evaluating Multi-attributes on Cause and Effect Relationship Visualization},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)},
year={2017},
pages={64-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006102300640074},
isbn={978-989-758-228-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)
TI - Evaluating Multi-attributes on Cause and Effect Relationship Visualization
SN - 978-989-758-228-8
AU - Bae J.
AU - Ventocilla E.
AU - Riveiro M.
AU - Helldin T.
AU - Falkman G.
PY - 2017
SP - 64
EP - 74
DO - 10.5220/0006102300640074