IEEE Trans Vis Comput Graph. 2018 Jan;24(1):402-412. doi: 10.1109/TVCG.2017.2744184. Epub 2017 Aug 29.
Traditional scatterplots fail to scale as the complexity and amount of data increases. In response, there exist many design options that modify or expand the traditional scatterplot design to meet these larger scales. This breadth of design options creates challenges for designers and practitioners who must select appropriate designs for particular analysis goals. In this paper, we help designers in making design choices for scatterplot visualizations. We survey the literature to catalog scatterplot-specific analysis tasks. We look at how data characteristics influence design decisions. We then survey scatterplot-like designs to understand the range of design options. Building upon these three organizations, we connect data characteristics, analysis tasks, and design choices in order to generate challenges, open questions, and example best practices for the effective design of scatterplots.
传统的散点图在数据的复杂性和数量增加时无法扩展。为了解决这个问题，出现了许多设计选项，它们可以修改或扩展传统的散点图设计，以适应更大的规模。这种广泛的设计选项为设计师和实践者带来了挑战，他们必须为特定的分析目标选择合适的设计。在本文中，我们帮助设计师为散点图可视化做出设计选择。我们调查文献，对散点图特有的分析任务进行编目。我们研究了数据特征如何影响设计决策。然后，我们调查了类似散点图的设计，以了解设计选项的范围。基于这三个组织，我们将数据特征、分析任务和设计选择联系起来，以便为散点图的有效设计生成挑战、开放性问题和示例最佳实践。
