CAREER: Effective Interaction Design for Data Visualization

职业：数据可视化的有效交互设计

• 批准号: 1942659
• 负责人: Arvind Satyanarayan
• 金额: $ 53.14万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942659&HistoricalAwards=false
• 关键词: CAREER; Effective; Interaction; Design; Data

This research extends the PI's unique and transformative Vega-Lite grammar and language for specifying information visualizations to focus on mechanisms for interaction. New tools will be integrated with popular open source / industry research authoring platforms. Prior research has developed theories of effective visual encoding (i.e., how best to map data values to visual properties such as position, shape, or size). Implementing these theories in software has advanced society's adoption of visualization as a medium for recording, analyzing, and communicating about data. However, there has been little analogous theory-building for interactivity, a critical component for enabling tight feedback between generating and answering hypotheses. For instance, how do different interaction design choices affect dataset coverage, the rate of insights, and people's confidence in their findings? Limits of prior theory impede support for interaction design in visualization systems, and establishing conventions for interaction design. For example, in different tools, dragging may pan a chart, highlight brushed points, or zoom into a selected region.This research will develop theory by evaluating interaction techniques for information visualization via crowdsourced, laboratory, and field studies. Design choices, data distributions, and analytic tasks will be investigated, vis-à-vis measurable outcomes, such as usability, completion time, accuracy, and higher-level cognition. The impact of resulting new theory on techniques for interactive visualization will be studied, addressing research questions such as: (1) How to present results to augment static visualizations with effective interactivity? (2) How to promote exploration? (3) How to suggest unexplored visualization states?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.

这项研究扩展了PI的独特和变革的Vega-Lite语法和语言，用于指定信息可视化，以专注于交互机制。新工具将与流行的开源/行业研究创作平台集成。 先前的研究已经发展了有效视觉编码的理论（即，如何最好地将数据值映射到视觉属性，例如位置、形状或大小）。在软件中实现这些理论已经促进了社会对可视化作为记录，分析和交流数据的媒介的采用。然而，很少有类似的理论建设的互动性，一个关键组成部分，使紧密的反馈之间产生和回答的假设。例如，不同的交互设计选择如何影响数据集覆盖率、洞察率以及人们对他们发现的信心？先验理论的局限性阻碍了可视化系统中交互设计的支持，也阻碍了交互设计的约定的建立。例如，在不同的工具中，拖动可以平移图表，突出显示画笔点，或放大到选定区域。本研究将通过众包，实验室和实地研究评估信息可视化的交互技术来发展理论。设计选择，数据分布和分析任务将被调查，维斯维斯可衡量的结果，如可用性，完成时间，准确性和更高层次的认知。由此产生的新理论对交互式可视化技术的影响将进行研究，解决研究问题，如：（1）如何提出的结果，以增强静态可视化与有效的交互性？(2)如何推进探索？(3)如何建议未探索的可视化状态？该奖项反映了NSF的法定使命，并被认为是值得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的支持。

项目成果

Rich Screen Reader Experiences for Accessible Data Visualization

丰富的屏幕阅读器体验，实现可访问的数据可视化

• DOI: 10.1111/cgf.14519
• 发表时间: 2022
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: Zong, Jonathan;Lee, Crystal;Lundgard, Alan;Jang, JiWoong;Hajas, Daniel;Satyanarayan, Arvind
• 通讯作者: Satyanarayan, Arvind

DIEL: Interactive Visualization Beyond the Here and Now

DIEL：超越此时此地的交互式可视化

• 发表时间: 2021
• 期刊: IEEE Vis
• 作者: Wu, Yifan;Chang, Remco;Hellerstein, Joseph;Satyanarayan, Arvind;Wu, Eugene
• 通讯作者: Wu, Eugene

B2: Bridging Code and Interactive Visualization in Computational Notebooks

• DOI: 10.1145/3379337.3415851
• 发表时间: 2020-10
• 期刊: Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology
• 作者: Yifan Wu;J. Hellerstein;Arvind Satyanarayan

Lyra 2: Designing Interactive Visualizations by Demonstration

Lyra 2：通过演示设计交互式可视化

• DOI: 10.1109/tvcg.2020.3030367
• 发表时间: 2021
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Zong, Jonathan;Barnwal, Dhiraj;Neogy, Rupayan;Satyanarayan, Arvind
• 通讯作者: Satyanarayan, Arvind

Representing Real-Time Multi-User Collaboration in Visualizations

在可视化中表示实时多用户协作

• 发表时间: 2020
• 期刊: IEEE Visualization (VIS
• 作者: Neogy, Rupayan;Zong, Jonathan;Satyanarayan, Arvind.
• 通讯作者: Satyanarayan, Arvind.