CAREER: HCC: Developing Perceptually-Driven Tools for Estimating Visualization Effectiveness

职业：HCC：开发用于估计可视化效果的感知驱动工具

• 批准号: 2320920
• 负责人: Danielle Szafir
• 金额: $ 54.99万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2028-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320920&HistoricalAwards=false
• 关键词: CAREER; HCC; Developing; Perceptually; Driven

Diverse people use visualizations to explore and communicate data across applications ranging from personal finance and activity tracking to public policy and scientific communication. New visualization techniques emerge as society's reliance on data grows. The ways these techniques represent data affect what people see in their data: different designs support different statistical insight and can even mislead people by appearing to show patterns that are not there. Designers lack grounded, actionable guidance for when and how their visualizations might most effectively communicate the patterns they see that matter most in their data. This project will work to develop data-driven models, metrics, and tools that describe what people see in a visualization and help them assess what it means and how it can help. Outcomes are expected to help people creating or using visualizations to rapidly predict what kinds of statistical patterns a visualization might communicate and the potential for biases in these patterns. This includes significantly reducing the time and expertise barriers for creating effective visualizations, leading to more precise and trustworthy public communication through data. The project will work to develop educational materials that teach best practices in visualization evaluation. These activities will collectively make it easier to develop honest and effective data visualizations across a range of goals and disciplines.The project investigates three core technical objectives: (1) modeling how people perceive different statistical properties of data across common representations, by performing a series of experiments modeling how people interpret statistical quantities in visualizations, leading to a curated corpus of experimental data for visualization perception; (2) generating metrics that describe the information people are most likely to gain from a visualization by conducting a series of mixed-methods experiments, paired with data from the first objective, to build models for probabilistically predicting visualization effectiveness; and (3) integrating these metrics into an interactive web tool for automating evaluation, by rapidly estimating the effectiveness of a given visualization for communicating a target set of properties. A crossover between visualization and vision science through interdisciplinary initiatives is planned, which includes the development and dissemination of educational materials released through open-source curriculum, an online course, and a textbook.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.

从个人理财和活动跟踪到公共政策和科学传播，各种各样的人使用可视化来探索和交流应用程序中的数据。随着社会对数据依赖的增长，新的可视化技术应运而生。这些技术表示数据的方式会影响人们在数据中看到的东西：不同的设计支持不同的统计见解，甚至可能通过显示不存在的模式来误导人们。对于何时以及如何使可视化最有效地传达他们在数据中看到的最重要的模式，设计师缺乏可靠的、可操作的指导。该项目将致力于开发数据驱动的模型、指标和工具，以描述人们在可视化中看到的内容，并帮助他们评估其含义以及如何提供帮助。期望结果能够帮助人们创建或使用可视化来快速预测可视化可能传达的统计模式以及这些模式中可能存在的偏差。这包括大大减少创建有效可视化的时间和专业知识障碍，从而通过数据实现更精确和可信赖的公共通信。该项目将致力于开发教育材料，教授可视化评估的最佳实践。这些活动将使跨一系列目标和学科开发诚实和有效的数据可视化变得更加容易。该项目研究了三个核心技术目标：(1)通过执行一系列模拟人们如何在可视化中解释统计量的实验，模拟人们如何感知跨常见表示的数据的不同统计属性，从而形成可视化感知的实验数据的精选语料库；(2)通过进行一系列混合方法实验，结合第一个目标的数据，生成描述人们最有可能从可视化中获得的信息的指标，以建立概率预测可视化效果的模型；(3)将这些指标集成到一个交互式网络工具中，通过快速评估给定可视化的有效性来传达目标属性集，从而实现自动化评估。计划通过跨学科的倡议将可视化和视觉科学交叉起来，其中包括通过开源课程、在线课程和教科书发布的教育材料的开发和传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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

Data, Data, Everywhere: Uncovering Everyday Data Experiences for People with Intellectual and Developmental Disabilities

数据，数据，无处不在：为智力和发育障碍人士揭示日常数据体验

• DOI: 10.1145/3544548.3581204
• 发表时间: 2023
• 期刊: SIGCHI Conference on Human Factors in Computing Systems
• 作者: Wu, Keke;Tran, Michelle Ho;Petersen, Emma;Koushik, Varsha;Szafir, Danielle Albers
• 通讯作者: Szafir, Danielle Albers

Measuring Categorical Perception in Color-Coded Scatterplots

• DOI: 10.1145/3544548.3581416
• 发表时间: 2023-03
• 期刊: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
• 作者: Chin Tseng;Ghulam Jilani Quadri;Zeyu Wang;D. Szafir

Cultivating Visualization Literacy for Children Through Curiosity and Play

通过好奇心和游戏培养孩子的可视化素养

• DOI: 10.1109/tvcg.2022.3209442
• 发表时间: 2023
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Bae, S. Sandra;Vanukuru, Rishi;Yang, Ruhan;Gyory, Peter;Zhou, Ran;Do, Ellen Yi-Luen;Szafir, Danielle Albers
• 通讯作者: Szafir, Danielle Albers

Scholastic: Graphical Human-AI Collaboration for Inductive and Interpretive Text Analysis

Scholastic：用于归纳和解释文本分析的图形化人机协作

• DOI: 10.1145/3526113.3545681
• 发表时间: 2022
• 期刊: ACM Symposium on User Interface Software and Technology (UIST
• 作者: Hong, Matt-Heun;Marsh, Lauren A.;Feuston, Jessica L.;Ruppert, Janet;Brubaker, Jed R.;Szafir, Danielle Albers
• 通讯作者: Szafir, Danielle Albers