Scatterplots: Tasks, Data, and Designs
Scatterplots: Tasks, Data, and Designs
复制标题
DOI：
10.1109/tvcg.2017.2744184
复制
发表时间：
2018
影响因子：
5.2
通讯作者：
Alper Sarıkaya;Michael Gleicher
中科院分区：
文献类型：
--
作者：
Alper Sarıkaya;Michael Gleicher
传统的散点图无法随着复杂性和数据量的增加而扩展。作为回应，存在许多设计选项，修改或扩展传统的散点图设计，以满足这些更大的尺度。这种设计选项的广度为设计人员和从业人员带来了挑战，他们必须为特定的分析目标选择适当的设计。在本文中，我们帮助设计师在设计散点图可视化的选择。我们调查的文献目录散点图特定的分析任务。我们来看看数据特征如何影响设计决策。然后，我们调查散点图样的设计，以了解设计选项的范围。在这三个组织的基础上，我们将数据特征、分析任务和设计选择联系起来，以产生挑战、开放性问题和有效设计散点图的最佳实践范例。
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.