Abstract Scatterplots can be used for a wide range of visual analysis tasks, for example comparing correlations or variances of clusters across potentially multiple classes of data, in order to find answers to higher-level questions. Comparing classes of data in one scatterplot demands additional visual channels to encode this dimension. While perception research suggests colors as rather perceptually dominant, other studies show that shapes can also be visually salient. However, with an increasing amount of data, overlapping shapes can cause perceptual difficulties and obscure data. Even though shapes in scatterplots have been investigated extensively, the overlap between these shapes has usually been avoided by using synthetic scatterplots. To overcome this limitation, we investigate the perceptual implications of overlap when comparing data using scatterplots using a series of crowd-sourced user studies. These studies include common visual analysis tasks, like comparing the number of points, comparing mean values, and determine the set of points that is more clustered. To support our investigations, we introduced and compared four metrics for overlap in scatterplots. Our results provide insight into the overlap in scatterplots, recommend combinations of shapes that are less prone to overlap, and outline how our metrics could be used to optimize future scatterplot design.

中文翻译：

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关于形状重叠对使用散点图进行数据比较的感知影响

摘要 散点图可用于广泛的视觉分析任务，例如比较潜在的多类数据中聚类的相关性或方差，以便找到更高级别问题的答案。比较一个散点图中的数据类别需要额外的视觉通道来编码这一维度。虽然感知研究表明颜色在感知上占主导地位，但其他研究表明形状也可以在视觉上显着。然而，随着数据量的增加，重叠的形状会导致感知困难和数据模糊。尽管散点图中的形状已被广泛研究，但通常通过使用合成散点图来避免这些形状之间的重叠。为了克服这个限制，我们通过一系列众包用户研究使用散点图来比较数据时重叠的感知影响。这些研究包括常见的视觉分析任务，例如比较点数、比较平均值以及确定更聚类的点集。为了支持我们的调查，我们引入并比较了散点图中重叠的四个指标。我们的结果提供了对散点图重叠的洞察，推荐了不太容易重叠的形状组合，并概述了如何使用我们的指标来优化未来的散点图设计。为了支持我们的调查，我们引入并比较了散点图中重叠的四个指标。我们的结果提供了对散点图重叠的洞察，推荐了不太容易重叠的形状组合，并概述了如何使用我们的指标来优化未来的散点图设计。为了支持我们的调查，我们引入并比较了散点图中重叠的四个指标。我们的结果提供了对散点图重叠的洞察，推荐了不太容易重叠的形状组合，并概述了如何使用我们的指标来优化未来的散点图设计。