Redundant is Not Redundant: Automating Efficient Categorical Palette Design Unifying Color&Shape Encodings with CatPAW

2026-02-06

AI Summary

This study addresses the lack of systematic understanding and design guidance regarding the effectiveness of redundant encoding using color and shape in categorical visualizations. Through four crowdsourced experiments, we systematically evaluate the perceptual performance of color–shape combinations across varying numbers of categories, revealing a significant interaction effect between the two channels. Our findings indicate that redundant encoding is most effective for datasets containing 5 to 8 categories. Building on these empirical results, we introduce CatPAW, an interactive tool that enables designers to automatically generate perceptually optimized categorical palettes. The work not only demonstrates that redundant encoding significantly improves accuracy in judging category relatedness but also provides a data-driven methodology and practical tool to support visualization design.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction
• Knowledge Representation and Reasoning: Preferences

Application Category

• Web Mining and Content Analysis: Web data visualization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Colors and shapes are commonly used to encode categories in multi-class scatterplots. Designers often combine the two channels to create redundant encodings, aiming to enhance class distinctions. However, evidence for the effectiveness of redundancy remains conflicted, and guidelines for constructing effective combinations are limited. This paper presents four crowdsourced experiments evaluating redundant color-shape encodings and identifying high-performing configurations across different category numbers. Results show that redundancy significantly improves accuracy in assessing class-level correlations, with the strongest benefits for 5-8 categories. We also find pronounced interaction effects between colors and shapes, underscoring the need for careful pairing in designing redundant encodings. Drawing on these findings, we introduce a categorical palette design tool that enables designers to construct empirically grounded palettes for effective categorical visualization. Our work advances understanding of categorical perception in data visualization by systematically identifying effective redundant color-shape combinations and embedding these insights into a practical palette design tool.

Problem

• redundant encoding
• categorical visualization
• color-shape combination
• multi-class scatterplots
• category perception

Innovation

• redundant encoding
• color-shape interaction
• categorical palette design
• crowdsourced evaluation
• CatPAW