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Title: Measuring the Separability of Shape, Size, and Color in Scatterplots
Scatterplots commonly use multiple visual channels to encode multivariate datasets. Such visualizations often use size, shape, and color as these dimensions are considered separable--dimensions represented by one channel do not significantly interfere with viewers' abilities to perceive data in another. However, recent work shows the size of marks significantly impacts color difference perceptions, leading to broader questions about the separability of these channels. In this paper, we present a series of crowdsourced experiments measuring how mark shape, size, and color influence data interpretation in multiclass scatterplots. Our results indicate that mark shape significantly influences color and size perception, and that separability among these channels functions asymmetrically: shape more strongly influences size and color perceptions in scatterplots than size and color influence shape. Models constructed from the resulting data can help designers anticipate viewer perceptions to build more effective visualizations.  more » « less
Award ID(s):
1657599
NSF-PAR ID:
10111568
Author(s) / Creator(s):
;
Date Published:
Journal Name:
CHI '19 Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Page Range / eLocation ID:
Paper No. 669
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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