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.

翻译：在多类别散点图中，色彩与形状常被用于编码不同类别。设计者通常将这两种视觉通道结合以创建冗余编码，旨在增强类别区分度。然而，关于冗余编码有效性的证据仍存在矛盾，且构建有效组合的设计准则较为有限。本文通过四项众包实验评估了冗余色彩-形状编码方案，并识别了在不同类别数量下表现优异的配置组合。实验结果表明，冗余编码能显著提升类别间相关性判读的准确度，在5-8个类别场景中增益最为明显。我们还发现色彩与形状之间存在显著的交互效应，这强调了在设计冗余编码时需要谨慎配对视觉元素。基于这些发现，我们开发了一款分类调色板设计工具，使设计者能够构建基于实证依据的调色板，以实现高效分类可视化。本研究通过系统识别有效的冗余色彩-形状组合，并将这些发现嵌入实用化调色板设计工具，推进了数据可视化中分类感知机制的理解。