CHS: Small: Collaborative Research: Representing and Learning Visualization Design Knowledge

CHS：小型：协作研究：表示和学习可视化设计知识

• 批准号: 1907941
• 负责人: Jessica Hullman
• 金额: $ 25万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907941&HistoricalAwards=false
• 关键词: CHS; Small; Collaborative; Research; Representing

This project contributes new methods and software tools for creating data-driven visualizations that improve the clarity and effectiveness of visual analysis and communication of data. Many visualization design guidelines, like "avoid highly saturated colors", or "start bars in a bar chart at 0", stem from empirical studies of how well people can read visualizations of various types. However, these guidelines are often stated informally in books or articles. In designing a visualization, an author may have to make decisions that prioritize one design guideline over another, yet the informal nature of such principles does not provide sufficient guidance for how to do this. Even when visualization researchers and system designers represent design guidelines in more formal "knowledge bases" that an authoring system can use to guide visualization authors towards more effective graphs, the guidelines are based on a person carefully summarizing the empirical results, an error-prone process. This project addresses these challenges to formulating and applying visualization design knowledge by creating new methods to identify, aggregate, edit, test, and search visualization design knowledge. This research will also address gaps in existing visualization design knowledge, applying novel methods to formulate and assess design guidelines for creating effective "multiple-view" visualizations (such as analysis dashboards or sequential presentations), visualizing very large datasets, and visually expressing uncertainty or error in data. We will create knowledge bases containing guidelines for these types of visualizations as well as an authoring tool to help authors manage competing design considerations between single and multiple views when designing visualizations like dashboards. All experimental results, knowledge bases, and authoring tools developed in this research will be made freely and publicly available.To meet these goals, this project develops a set of methods for identifying and evaluating visualization design guidelines from empirical research on visualization perception and interpretation. To do this, the team will develop ways to re-express existing results from relevant experimental literature on graphical perception and cognition as constraints, and create new methods and tools for directly eliciting design guidelines from visualization experts such as skilled designers or researchers. The project will also produce automated methods for generating visualizations and collecting task-specific visualization judgments in order to learn appropriate priority weights for a given set of design constraints. By developing representations and models for capturing empirical results that can account for the uncertainty that is inherent in results from human subjects experiments, the project stands to synthesize and clarify existing empirical knowledge about visualization design. The research will also advance the state of the art in visualization design knowledge by contributing fundamental methods for (1) identifying and learning guidelines for large dataset visualizations, multiple view visualizations like dashboards, and uncertainty visualizations, and (2) exploring effective interface designs for browsing, editing, and testing visualization knowledge bases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目为创建数据驱动的可视化提供了新的方法和软件工具，以提高可视化分析和数据交流的清晰度和有效性。许多可视化设计指南，如“避免高度饱和的颜色”，或“条形图中从0开始的条形图”，源于对人们阅读各种类型的可视化效果的经验研究。然而，这些准则通常是在书籍或文章中非正式地陈述的。在设计可视化时，作者可能不得不做出决定，将一个设计指导原则优先于另一个设计指导原则，然而这种原则的非正式性质并不能为如何做到这一点提供足够的指导。即使可视化研究人员和系统设计人员在更正式的“知识库”中表示设计指南，创作系统可以使用这些知识库来指导可视化作者走向更有效的图形，这些指南也是基于一个人仔细总结经验结果的基础上的，这是一个容易出错的过程。该项目通过创建识别、聚合、编辑、测试和搜索可视化设计知识的新方法来解决制定和应用可视化设计知识方面的这些挑战。这项研究还将解决现有可视化设计知识的空白，应用新的方法来制定和评估设计指南，以创建有效的“多视图”可视化(如分析仪表板或顺序演示)，可视化非常大的数据集，并直观地表达数据中的不确定性或错误。我们将创建包含这些类型的可视化指南的知识库以及一个创作工具，以帮助作者在设计仪表板等可视化时管理单视图和多视图之间的相互竞争的设计注意事项。本研究开发的所有实验结果、知识库和创作工具将免费公开。为了实现这些目标，本项目开发了一套从可视化感知和解释的经验研究中识别和评估可视化设计指南的方法。为了做到这一点，该团队将开发方法来重新表达有关图形感知和认知作为约束的相关实验文献中的现有结果，并创建新的方法和工具，以直接从熟练的设计师或研究人员等可视化专家那里获得设计指南。该项目还将产生用于生成可视化和收集特定于任务的可视化判断的自动化方法，以便为给定的一组设计约束学习适当的优先级权重。通过开发能够解释人体受试者实验结果中固有的不确定性的经验结果的表示和模型，该项目旨在综合和澄清关于可视化设计的现有经验知识。这项研究还将推动可视化设计知识的最新发展，为以下方面提供基本方法：(1)识别和学习大数据集可视化、多视图可视化(如仪表板)和不确定性可视化的指导方针，以及(2)探索用于浏览、编辑和测试可视化知识库的有效界面设计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Design Patterns and Trade‐Offs in Responsive Visualization for Communication

通信响应式可视化的设计模式和权衡

• DOI: 10.1111/cgf.14321
• 发表时间: 2021
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: Kim, Hyeok;Moritz, Dominik;Hullman, Jessica
• 通讯作者: Hullman, Jessica

Exploring the Effects of Aggregation Choices on Untrained Visualization Users' Generalizations From Data

• DOI: 10.1111/cgf.13902
• 发表时间: 2020-02
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: F. Nguyen;X. Qiao;Jeffrey Heer;J. Hullman

Cicero: A Declarative Grammar for Responsive Visualization

Cicero：响应式可视化的声明性语法

• DOI: 10.1145/3491102.3517455
• 发表时间: 2022
• 期刊: Proceedings of ACM CHI Conference on Human Factors in Computing Systems
• 作者: Kim, Hyeok;Rossi, Ryan;Du, Fan;Koh, Eunyee;Guo, Shunan;Hullman, Jessica;Hoffswell, Jane
• 通讯作者: Hoffswell, Jane

An Automated Approach to Reasoning About Task-Oriented Insights in Responsive Visualization

• DOI: 10.1109/tvcg.2021.3114782
• 发表时间: 2021-07
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Hyeok Kim;Ryan A. Rossi;Abhraneel Sarma;Dominik Moritz;J. Hullman

Visual Reasoning Strategies for Effect Size Judgments and Decisions

• DOI: 10.1109/tvcg.2020.3030335
• 发表时间: 2021-02-01
• 期刊: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
• 影响因子: 5.2
• 作者: Kale, Alex;Kay, Matthew;Hullman, Jessica
• 通讯作者: Hullman, Jessica