Revisiting Categorical Color Perception in Scatterplots: Sequential, Diverging, and Categorical Palettes

TL;DR

This paper empirically tests five color-palette families for encoding categorical data in multiclass scatterplots, focusing on mean-value judgments across 2–10 categories. Using a crowdsourced task with 20 palettes and ANOVA analyses, it finds hue-focused categorical palettes provide the strongest and most robust performance, though lightness variation and perceptual uniformity also influence outcomes, especially as category count increases. The study reconciles traditional hue-centric guidelines with evidence that perceptual distance and lightness interplay shape categorical perception, offering nuanced guidance for palette design. These findings inform practical scatterplot encoding decisions to improve categorical discrimination and data interpretation in visualization tasks.

Abstract

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.

Figures (2)

• Figure 1: Example stimuli for three palette types.
• Figure 2: Accuracy rates broken down by category number separated by color palette families. We found that categorical palettes were more robust to increasing numbers of categories, with other families dropping in performance for six or more categories.