Paper Info
Title
Evaluating Multi-Attributes On Cause And Effect Relationship Visualization
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.
Year
DOI
Venue
2017
10.5220/0006102300640074
PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 3
Keywords
Field
DocType
Cause and Effect, Uncertainty, Evaluation, Graph Visualization
Data science,Visualization,Computer science
Conference
Citations 
PageRank 
References 
5
0.44
0
Authors
5
Name
Order
Citations
PageRank
JuHee Bae1659.69
Elio Ventocilla251.12
Maria Riveiro313318.64
Tove Helldin4768.59
Göran Falkman517322.13