Measuring Categorical Perception in Color-Coded Scatterplots
Measuring Categorical Perception in Color-Coded Scatterplots
复制标题
DOI：
10.1145/3544548.3581416
复制
发表时间：
2023-03
期刊：
影响因子：
--
通讯作者：
Chin Tseng;Ghulam Jilani Quadri;Zeyu Wang;D. Szafir
中科院分区：
文献类型：
--
作者：
Chin Tseng;Ghulam Jilani Quadri;Zeyu Wang;D. Szafir
散点图通常使用颜色来编码分类数据。然而，随着数据集的大小和复杂性的增加，这些通道的功效可能会有所不同。设计师们缺乏对不同设计选择对类别数量变化的鲁棒性的洞察力。本文提出了一个众包实验，测量多类散点图中使用的类别数量和颜色编码的选择如何影响查看者跨类分析数据的能力。参与者在一系列散点图中估计相对平均值，其中2到10个类别使用从流行的设计工具中绘制的10个调色板进行编码。我们的研究结果表明，在调色板内的类别和颜色的可辨别性显着影响人们的分类数据的散点图的感知和判断变得更加困难的类别的数量的增长。我们研究现有的调色板设计的方法，根据我们的结果，以帮助设计师作出强大的颜色选择，他们的数据参数的通知。
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.