CRII: CHS: Data-Driven Automation of Color Encodings for Data Visualization

CRII：CHS：用于数据可视化的数据驱动的颜色编码自动化

基本信息

批准号：
1657599
负责人：
Danielle Szafir
金额：
$ 17.49万
依托单位：
University of Colorado at Boulder
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2020-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657599&HistoricalAwards=false
关键词：
CRII CHS Data Driven Automation

项目摘要

Graphs, charts, and other visualizations of data rely on color both to convey key aspects of the underlying data and to attract and engage viewers. Getting both the accuracy and aesthetics of color choices right, however, is hard, and most existing tools for helping designers focus on just one of the two. Developing accurate color mappings is even harder because how colors are perceived changes depending on the size and shape of visual marks, lighting and contrast, and a number of other factors. In this project, the research team will use designs created by existing tools to construct an initial statistical model of color mappings that captures expert designers' current decision-making. They will then improve those models by creating visualizations based on the models, altering size, shape, contrast, and lighting, and testing how well people can use those designs to learn the underlying values of the data. Finally, the team will create a design tool that allows both expert and non-expert designers to create visualizations, choosing anchor colors and aspects of the visualization, and generating color maps that are most accurate and aesthetic based on the models and the designer's choices. The work will lead to more accurate models of perception and mechanisms for choosing color maps that capture both design expertise and perceptual accuracy; this, in turn, will lead to practical improvements in the effectiveness of data visualizations that are increasingly part of people's experience. The team also plans to increase the accessibility of data visualizations by helping designers choose color mappings that are more usable by people with color-blindness, while making the tools themselves more usable by color-blind people. The tools and work will also be integrated into several courses on human-computer interaction and data science at the lead investigator's institution, benefiting students from a variety of research groups and departments.Color ramps will be represented as a set of control points (two end points in sequential encodings and two end points plus a midpoint in diverging ramps) that determine the overall structure of the ramp, and a smooth interpolation path that connecting the control points in colorspace. To capture current expert practice, the team will first extract initial color ramps from colormaps available in existing design-based visualization tools, using the CIELAB colorspace to model the statistical characteristics of the control points and interpolation paths of these encodings, generating aesthetic constraints grounded in the current design consensus. The team will then use crowdsourcing platforms, which have been shown to be effective for a number of perceptual and visualization experiments, to systematically study how specific aspects of visualization design including mark shape, mark size, and visualization type, affect people's ability to detect color differences in colorspace; further, conducting the experiment online means this model will be specifically tailored to the online/web/screen viewing context. This empirical model can enforce perceptual constraints imposed by visualization design choices on the color ramps generated by the aesthetic models by constraining and repositioning control points. Finally, these models will be integrated into a publicly available color authoring system that will be validated through use in courses at the lead researcher's institution and at design workshops with the local community. In addition to developing the specific models and tools around color encodings, the work sets up a broader research agenda of combining automation and interaction, in which semi-automated guidance democratizes effective visualization practice and allows people to leverage prior designs and create new representations without requiring extensive visualization training.

图形、图表和其他数据可视化都依赖颜色来传达基础数据的关键方面并吸引观众。然而，同时保证颜色选择的准确性和美观性是很困难的，大多数现有的帮助设计师只关注两者之一的工具。开发准确的颜色映射更加困难，因为感知颜色的方式会根据视觉标记的大小和形状、照明和对比度以及许多其他因素而变化。在这个项目中，研究团队将使用现有工具创建的设计来构建颜色映射的初始统计模型，以捕获专家设计师当前的决策。然后，他们将通过基于模型创建可视化、改变大小、形状、对比度和照明来改进这些模型，并测试人们如何使用这些设计来了解数据的潜在价值。最后，该团队将创建一个设计工具，允许专家和非专家设计师创建可视化效果，选择锚定颜色和可视化的各个方面，并根据模型和设计师的选择生成最准确和最美观的颜色图。这项工作将带来更准确的感知模型和选择色彩图的机制，以捕获设计专业知识和感知准确性；反过来，这将导致数据可视化的有效性得到实际提高，数据可视化越来越成为人们体验的一部分。该团队还计划通过帮助设计人员选择更适合色盲人士使用的颜色映射，同时使工具本身更适合色盲人士使用，来提高数据可视化的可访问性。这些工具和工作还将被整合到首席研究员所在机构的几门人机交互和数据科学课程中，使来自各个研究小组和部门的学生受益。颜色渐变将表示为一组控制点（两端顺序编码中的点和发散渐变中的两个端点加上中点）确定渐变的整体结构，以及连接色彩空间中的控制点的平滑插值路径。为了捕捉当前的专家实践，该团队将首先从现有基于设计的可视化工具中可用的色彩图中提取初始色彩渐变，使用 CIELAB 色彩空间对这些编码的控制点和插值路径的统计特征进行建模，生成基于目前的设计共识。然后，该团队将使用众包平台（该平台已被证明对许多感知和可视化实验有效）系统地研究可视化设计的具体方面（包括标记形状、标记大小和可视化类型）如何影响人们检测颜色的能力色彩空间的差异；此外，在线进行实验意味着该模型将专门针对在线/网络/屏幕观看环境进行定制。该经验模型可以通过约束和重新定位控制点，对美学模型生成的色带施加可视化设计选择所施加的感知约束。最后，这些模型将被集成到一个公开的色彩创作系统中，该系统将通过在首席研究人员机构的课程和当地社区的设计研讨会上的使用进行验证。除了开发围绕颜色编码的特定模型和工具之外，这项工作还建立了一个更广泛的结合自动化和交互的研究议程，其中半自动化指导使有效的可视化实践民主化，并允许人们利用先前的设计并创建新的表示，而无需广泛的可视化培训。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Color Crafting: Automating the Construction of Designer Quality Color Ramps

DOI：
10.1109/tvcg.2019.2934284
发表时间：
2020-01-01
期刊：
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
影响因子：
5.2
作者：
Smart, Stephen;Wu, Keke;Szafir, Danielle Albers
通讯作者：
Szafir, Danielle Albers

Measuring the Separability of Shape, Size, and Color in Scatterplots

DOI：
10.1145/3290605.3300899
发表时间：
2019-05
期刊：
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
影响因子：
0
作者：
Stephen Smart;D. Szafir
通讯作者：
Stephen Smart;D. Szafir

Where's My Data? Evaluating Visualizations with Missing Data