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

基本信息

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

项目摘要

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跨应用程序的数据，从个人财务和活动跟踪到公共政策和科学传播。随着社会对数据依赖的增加，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洞察力，甚至可能通过显示不存在的模式来误导人们。设计师缺乏扎实的、可操作的指导，无法确定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，并帮助他们评估其含义及其提供的帮助。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类型以及这些模式中存在偏差的可能性。这包括显着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值得信赖的公共沟通。该项目将致力于开发教授可视化评估最佳实践的教育材料。这些活动共同将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。该项目研究三个核心技术目标：（1）通过执行一系列操作，对人们如何感知常见表示形式的数据的不同统计属性进行建模模拟人们如何解释可视化中的统计量的实验，从而形成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，与来自第一个目标的数据配对，生成描述人们最有可能从可视化中获得的信息的指标，以建立概率预测可视化效果的模型； (3) 通过快速估计给定可视化用于传达目标属性集的有效性，将这些指标集成到交互式网络工具中以进行自动化评估。计划通过跨学科举措实现可视化和视觉科学之间的交叉，其中包括开发和传播通过开源课程发布的教育材料、在线课程和教科书。该奖项反映了 NSF 的法定使命，并被认为值得支持通过使用基金会的智力优点和更广泛的影响审查标准进行评估。

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）

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
通讯作者：
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
影响因子：
0
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

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

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