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.

各种各样的人使用可视化来探索和传达从个人理财和活动跟踪到公共政策和科学沟通等应用程序的数据。随着社会对数据的依赖的增长，新的可视化技术出现了。这些技术代表数据的方式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。设计师缺乏何时以及如何最有效地传达其数据中最重要的模式的基础，可行的指导。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。结果有望帮助人们创建或使用可视化，以快速预测可视化可能会传达哪些统计模式，并在这些模式中产生偏见的潜力。这包括大大减少创建有效可视化的时间和专业知识障碍，从而通过数据进行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。这些活动将集体使得在一系列目标和学科之间进行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）建模人们如何通过一系列实验来模拟人们如何在可视化数据中解释统计数量，从而实现统计数量，从而导致实验数据的策划数据，从而对可视化的统计量进行建模；（2）生成描述人们最有可能通过进行一系列混合方法实验的指标从可视化中获得，并与第一个目标的数据配对，以构建模型，以预测可视化的效果；（3）通过快速估计给定可视化在通信目标集属性集的有效性，将这些指标集成到用于自动化评估的交互式Web工具中。计划通过跨学科的举措之间的可视化和视觉科学之间的跨界，其中包括通过开放式课程发布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的法定任务，并被认为是通过基金会的智力优点和广泛影响的评估来评估的值得通过评估的支持。

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