权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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依赖于颜色来传达底层数据的关键方面，并吸引和吸引观众。然而，要同时做到颜色选择的准确性和美观性是很困难的，大多数现有的工具都可以帮助设计师专注于两者之一。开发准确的颜色映射甚至更难，因为颜色的感知方式取决于视觉标记的大小和形状，照明和对比度以及许多其他因素。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，以捕获专家设计师的当前决策。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，并测试人们如何使用这些设计来学习数据的潜在价值来改进这些模型。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，选择可视化的锚颜色和方面，并根据模型和设计师的选择生成最准确和美观的颜色图。这项工作将导致更准确的感知模型和机制，用于选择同时捕获设计专业知识和感知准确性的彩色地图;这反过来又将导致数据可视化的有效性的实际改进，这些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师选择色盲人群更可用的颜色映射来增加数据可视化的可访问性，同时使工具本身更适合色盲人群。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，使来自各种研究小组和部门的学生受益。颜色斜坡将被表示为一组控制点（顺序编码中的两个端点和发散斜坡中的两个端点加上中点），其确定斜坡的总体结构，以及连接颜色空间中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的基于设计的可视化工具中可用的色彩图中提取初始颜色渐变，使用CIELAB色彩空间来建模这些编码的控制点和插值路径的统计特征，生成基于当前设计共识的美学约束。然后，该团队将使用众包平台，这些平台已被证明对一些感知和可视化实验是有效的，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，如何影响人们在色彩空间中检测颜色差异的能力;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。该经验模型可以通过约束和重新定位控制点来对由美学模型生成的颜色渐变实施由可视化设计选择施加的感知约束。最后，这些模型将被集成到一个公开的颜色创作系统，将通过使用在课程中的首席研究员的机构和设计研讨会与当地社区进行验证。除了围绕颜色编码开发特定的模型和工具外，这项工作还建立了一个更广泛的研究议程，将自动化和交互相结合，其中半自动化的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，而无需进行广泛的可视化培训。

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