Raidou Renata G, Groller M Eduard, Eisemann Martin
IEEE Trans Vis Comput Graph. 2019 Jun;25(6):2205-2216. doi: 10.1109/TVCG.2019.2903956. Epub 2019 Mar 15.
Scatter plots are the most commonly employed technique for the visualization of bivariate data. Despite their versatility and expressiveness in showing data aspects, such as clusters, correlations, and outliers, scatter plots face a main problem. For large and dense data, the representation suffers from clutter due to overplotting. This is often partially solved with the use of density plots. Yet, data overlap may occur in certain regions of a scatter or density plot, while other regions may be partially, or even completely empty. Adequate pixel-based techniques can be employed for effectively filling the plotting space, giving an additional notion of the numerosity of data motifs or clusters. We propose the Pixel-Relaxed Scatter Plots, a new and simple variant, to improve the display of dense scatter plots, using pixel-based, space-filling mappings. Our Pixel-Relaxed Scatter Plots make better use of the plotting canvas, while avoiding data overplotting, and optimizing space coverage and insight in the presence and size of data motifs. We have employed different methods to map scatter plot points to pixels and to visually present this mapping. We demonstrate our approach on several synthetic and realistic datasets, and we discuss the suitability of our technique for different tasks. Our conducted user evaluation shows that our Pixel-Relaxed Scatter Plots can be a useful enhancement to traditional scatter plots.
散点图是可视化双变量数据最常用的技术。尽管散点图在展示数据特征（如聚类、相关性和异常值）方面具有通用性和表现力，但它面临一个主要问题。对于大型密集数据，由于重叠绘制，其表示会受到杂乱的影响。这通常通过使用密度图来部分解决。然而，在散点图或密度图的某些区域可能会出现数据重叠，而其他区域可能部分甚至完全为空。可以采用适当的基于像素的技术来有效填充绘图空间，从而额外给出数据模式或聚类数量的概念。我们提出了像素松弛散点图，这是一种新的简单变体，通过基于像素的空间填充映射来改进密集散点图的显示。我们的像素松弛散点图能更好地利用绘图画布，同时避免数据重叠，并在数据模式的存在和大小方面优化空间覆盖和洞察力。我们采用了不同的方法将散点图点映射到像素并直观呈现这种映射。我们在几个合成数据集和真实数据集上展示了我们的方法，并讨论了我们的技术对不同任务的适用性。我们进行的用户评估表明，我们的像素松弛散点图可以是对传统散点图的有益增强。
