(0.59MB) Stephen Smart & Danielle Albers Szafir. Measuring the Separability of Shape, Size, and Color in Scatterplots. ACM CHI Conference on Human Factors in Computing Systems, 2019 (to appear). [Published as part of the Proceedings of ACM CHI Conference on Human Factors in Computing Systems 2019]
Abstract: 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.
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