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: 2046725
• 负责人: Danielle Szafir
• 金额: $ 54.99万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046725&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.

不同的人使用可视化来探索和交流数据，从个人财务和活动跟踪到公共政策和科学交流。随着社会对数据依赖的增长，新的可视化技术出现了。这些技术表示数据的方式会影响人们在数据中看到的内容：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。设计师缺乏扎实、可操作的指导，无法了解他们的可视化何时以及如何最有效地传达他们在数据中看到的最重要的模式。该项目将致力于开发数据驱动的模型，指标和工具，描述人们在可视化中看到的内容，并帮助他们评估它的含义以及它如何提供帮助。预计结果将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的偏见。这包括显著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、更值得信赖的公共沟通。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。这些活动将共同使跨一系列目标和学科开发诚实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）通过执行对人们如何解释可视化中的统计量进行建模的一系列实验，对人们如何感知跨公共表示的数据的不同统计属性进行建模，产生用于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，与来自第一目标的数据配对，生成描述人们最有可能从可视化中获得的信息的度量，以建立用于概率性地预测可视化效果的模型;以及（3）通过快速估计用于传达目标属性集的给定可视化的效果，将这些度量集成到用于自动化评估的交互式web工具中。通过跨学科的计划，可视化和视觉科学之间的交叉，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NSF的法定使命，并通过使用基金会的智力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

The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots

• DOI: 10.1109/tvcg.2021.3114783
• 发表时间: 2021-08
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Matt-Heun Hong;J. Witt;D. Szafir

Making Data Tangible: A Cross-disciplinary Design Space for Data Physicalization

让数据变得有形：数据物理化的跨学科设计空间

• DOI: 10.1145/3491102.3501939
• 发表时间: 2022
• 期刊: ACM SIGCHI Conference on Human Factors in Computing Systems (CHI
• 作者: Bae, S. Sandra;Zheng, Clement;West, Mary Etta;Do, Ellen Yi-Luen;Huron, Samuel;Szafir, Danielle Albers
• 通讯作者: Szafir, Danielle Albers

Professional Differences: A Comparative Study of Visualization Task Performance and Spatial Ability Across Disciplines

专业差异：跨学科可视化任务表现和空间能力的比较研究

• DOI: 10.1109/tvcg.2021.3114805
• 发表时间: 2022
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Hall, Kyle Wm.;Kouroupis, Anthony;Bezerianos, Anastasia;Szafir, Danielle Albers;Collins, Christopher
• 通讯作者: Collins, Christopher

Visualizing Temperature Trends: Higher Sensitivity to Trend Direction With Single-Hue Palettes

• DOI: 10.1037/xap0000411
• 发表时间: 2022-02-17
• 期刊: JOURNAL OF EXPERIMENTAL PSYCHOLOGY-APPLIED
• 影响因子: 2.6
• 作者: Warden，Amelia C.;Witt，Jessica K.;Szafir，Danielle Albers
• 通讯作者: Szafir，Danielle Albers