Scatterplots commonly use color to encode categorical data. However, as datasets increase in size and complexity, the efficacy of these channels may vary. Designers lack insight into how robust different design choices are to variations in category numbers. This paper presents a crowdsourced experiment measuring how the number of categories and choice of color encodings used in multiclass scatterplots influences the viewers' abilities to analyze data across classes. Participants estimated relative means in a series of scatterplots with 2 to 10 categories encoded using ten color palettes drawn from popular design tools. Our results show that the number of categories and color discriminability within a color palette notably impact people's perception of categorical data in scatterplots and that the judgments become harder as the number of categories grows. We examine existing palette design heuristics in light of our results to help designers make robust color choices informed by the parameters of their data.

翻译：散点图通常使用颜色对分类数据进行编码。然而，随着数据集规模和复杂度的增加，这些通道的有效性可能发生变化。设计者缺乏对不同设计选择在类别数量变化时的鲁棒性的了解。本文通过一项众包实验，测量了多类散点图中类别数量及颜色编码选择如何影响观察者跨类别分析数据的能力。参与者对使用来自流行设计工具的十种调色板、包含2到10个类别的一系列散点图中的相对均值进行估计。我们的结果表明，调色板中的类别数量和颜色可分辨性显著影响人们对散点图中类别数据的感知，且随着类别数量的增加，判断变得更为困难。我们根据实验结果审视了现有的调色板设计启发式方法，以帮助设计者根据数据参数做出鲁棒的颜色选择。