Revisiting Categorical Color Perception in Scatterplots: Sequential, Diverging, and Categorical Palettes
arxiv(2024)
摘要
Existing guidelines for categorical color selection are heuristic, often
grounded in intuition rather than empirical studies of readers' abilities.
While design conventions recommend palettes maximize hue differences, more
recent exploratory findings indicate other factors, such as lightness, may play
a role in effective categorical palette design. We conducted a crowdsourced
experiment on mean value judgments in multi-class scatterplots using five color
palette families–single-hue sequential, multi-hue sequential,
perceptually-uniform multi-hue sequential, diverging, and multi-hue
categorical–that differ in how they manipulate hue and lightness. Participants
estimated relative mean positions in scatterplots containing 2 to 10 categories
using 20 colormaps. Our results confirm heuristic guidance that hue-based
categorical palettes are most effective. However, they also provide additional
evidence that scalable categorical encoding relies on more than hue variance.
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