Measuring the Separability of Shape, Size, and Color in Scatterplots
Measuring the Separability of Shape, Size, and Color in Scatterplots
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DOI：
10.1145/3290605.3300899
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发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
Stephen Smart;D. Szafir
中科院分区：
文献类型：
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作者：
Stephen Smart;D. Szafir
散点图通常使用多个视觉通道来编码多变量数据集。这种可视化通常使用大小、形状和颜色，因为这些维度被认为是可分离的--由一个通道表示的维度不会显著干扰查看者感知另一个通道中数据的能力。然而，最近的工作表明，标记的大小显着影响色差的看法，导致更广泛的问题，这些通道的可分性。在本文中，我们提出了一系列众包实验测量标记的形状，大小和颜色如何影响多类散点图中的数据解释。我们的研究结果表明，标记的形状显着影响颜色和大小的感知，这些通道之间的功能不对称的可分性：形状更强烈地影响大小和颜色的看法比大小和颜色的散点图影响形状。从结果数据构建的模型可以帮助设计师预测观众的看法，以建立更有效的可视化。
Scatterplots commonly use multiple visual channels to encode multivariate datasets. Such visualizations often use size, shape, and color as these dimensions are considered separable--dimensions represented by one channel do not significantly interfere with viewers' abilities to perceive data in another. However, recent work shows the size of marks significantly impacts color difference perceptions, leading to broader questions about the separability of these channels. In this paper, we present a series of crowdsourced experiments measuring how mark shape, size, and color influence data interpretation in multiclass scatterplots. Our results indicate that mark shape significantly influences color and size perception, and that separability among these channels functions asymmetrically: shape more strongly influences size and color perceptions in scatterplots than size and color influence shape. Models constructed from the resulting data can help designers anticipate viewer perceptions to build more effective visualizations.