Authors:
Juhee Bae
;
Elio Ventocilla
;
Maria Riveiro
;
Tove Helldin
and
Göran Falkman
Affiliation:
University of Skövde, Sweden
Keyword(s):
Cause and Effect, Uncertainty, Evaluation, Graph Visualization.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
Graph Visualization
;
Information and Scientific Visualization
;
Perception and Cognition in 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 w
ith numbers or with width and brightness.
(More)