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Prediction of data visibility in two-dimensional scatterplots

机译：二维散点图中的数据可见性预测

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The result of a visualization process depends on the user's decisions along it. With the intention of accelerating this process and guaranteeing an appropriate visualization of the data, we are looking to semi-automatize the process to help the users with the decision-making along it. To contribute to this semi-automation, it is useful to have metrics that characterize different important aspects of the visualization techniques, such as data representation visibility. Besides, scatterplots are a widely used technique to visualize scalar datasets. In this context, this work presents a metric that evaluates data representation visibility considering glyph visibility in scatterplots. We defined a metric that estimates the proportion of glyphs that will be visible regardless of the drawing order, and it depends on the number of items in the dataset, the size of the window, and the size of the glyphs that will represent the data. To define and approximate the metric, we experimented with several random datasets for which both dimensions followed a normal distribution. This metric constitutes an alternative to characterize scatterplots and collaborates in the semi-automation of the user's decisions along the visualization process.
机译：可视化过程的结果取决于用户的决策。为了加快此过程并确保对数据进行适当的可视化，我们正在寻求半自动化过程，以帮助用户做出决策。为了促进这种半自动化，拥有可表征可视化技术不同重要方面的指标（例如数据表示可见性）非常有用。此外，散点图是一种广泛使用的可视化标量数据集的技术。在这种情况下，这项工作提出了一种度量，该度量考虑了散点图中的字形可见性来评估数据表示可见性。我们定义了一个度量标准，该度量标准估计不考虑绘制顺序的可见字形的比例，并且它取决于数据集中项目的数量，窗口的大小以及表示数据的字形的大小。为了定义和近似指标，我们尝试了两个维度都遵循正态分布的随机数据集。此度量标准是表征散点图的替代方法，并且可以在可视化过程中协作实现用户决策的半自动化。

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