RAPID: COVID-19 Information Visualizations

RAPID：COVID-19 信息可视化

• 批准号: 2030059
• 负责人: Priti Shah
• 金额: $ 20万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030059&HistoricalAwards=false
• 关键词: RAPID; COVID; 19; Information; Visualizations

COVID-19 has upended daily life across the globe. Government leaders, medical professions, and the media are communicating the impact of various public health measures such as social distancing by describing predictions of epidemiological models. Social media has been inundated with visualizations that have been created to help communicate the need for these measures. People’s everyday decisions, as well as their support of public health policy, will depend on their understanding of the COVID-19 pandemic. The research identifies the best way to communicate COVID-19 risk data to the public and to help people understand the potential impacts of different behaviors and policies. The public has many questions about what behaviors are safe. If the results show that simulations can help convey the information to the public, simulations that center on specific questions people are asking will be a valuable tool as people navigate the uncertainty surrounding COVID-19. The simulations are available to the general public and shared with the news media. People’s everyday decisions, as well as their support of public health policy, will depend on their understanding of the COVID-19 pandemic. Unfortunately, lack of understanding has led to claims that public health officials’ dire warnings are merely scare tactics of propaganda. In general, there is a fundamental misunderstanding and distrust in uncertain simulations of hypothetical data and outcomes. The current project develops visualizations for communicating important risk-related COVID epidemiological models to support comprehension and trust in science-based forecasts and recommendations and improving COVID-related decision making. The research tests key proposed visualization design features to assess their value in the current pandemic. The scholars also determine the influence of individual difference factors (numeracy, trust in science, and current anxiety levels) on the effectiveness of different visualization design features on comprehension of personal and global risk models, trust, and macro- (general actions such as social distancing) and micro-level (using a face mask while shopping) COVID-19 decisions asked before and after experience with the visualizations. The proposed research tests the generalizability of key cognitive principles to visualizations in a real-life context. While prior research has independently considered these factors in artificial contexts, limited work has addressed how these factors interact with each other, and also how the factors influence not only comprehension but also trust and behavioral intentions. If principles developed in these artificial contexts do not generalize to COVID, this would necessitate revision of risk visualization guidelines. Thus, the intellectual impact of this work is to improve our understanding of how to communicate complex risk models to individuals with varying backgrounds and prior beliefs.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.

COVID-19颠覆了地球仪的日常生活。政府领导人、医疗专业人士和媒体正在通过描述流行病学模型的预测来传达各种公共卫生措施的影响，例如社交距离。社交媒体上充斥着为帮助传达这些措施的必要性而创建的可视化内容。人们的日常决策，以及他们对公共卫生政策的支持，将取决于他们对COVID-19大流行的理解。该研究确定了向公众传达COVID-19风险数据的最佳方式，并帮助人们了解不同行为和政策的潜在影响。公众对什么行为是安全的有很多疑问。如果结果表明模拟可以帮助向公众传达信息，那么围绕人们提出的具体问题进行的模拟将成为人们应对COVID-19不确定性的宝贵工具。模拟结果可供公众使用，并与新闻媒体分享。 人们的日常决策，以及他们对公共卫生政策的支持，将取决于他们对COVID-19大流行的理解。不幸的是，由于缺乏理解，有人声称公共卫生官员的可怕警告只是宣传的恐吓策略。总的来说，对假设数据和结果的不确定模拟存在根本的误解和不信任。目前的项目开发了用于传达重要风险相关COVID流行病学模型的可视化，以支持对基于科学的预测和建议的理解和信任，并改善COVID相关决策。该研究测试了关键的可视化设计功能，以评估它们在当前流行病中的价值。学者们还确定了个体差异因素（计算能力，对科学的信任和当前的焦虑水平）对不同可视化设计特征在理解个人和全球风险模型，信任以及宏观（一般行动，如社交距离）和微观（购物时使用口罩）COVID-19决策方面的有效性的影响。拟议中的研究测试在现实生活中的可视化的关键认知原则的概括性。虽然先前的研究已经在人工环境中独立考虑了这些因素，但有限的工作已经解决了这些因素如何相互作用，以及这些因素如何不仅影响理解，而且影响信任和行为意图。如果在这些人为背景下制定的原则不能推广到COVID，那么就有必要修订风险可视化指南。因此，这项工作的智力影响是提高我们对如何向具有不同背景和先前信仰的个人传达复杂风险模型的理解。该奖项反映了NSF的法定使命，并且通过使用基金会的智力评估被认为值得支持优点和更广泛的影响审查标准。

项目成果

Dynamic ensemble visualizations to support understanding for uncertain trajectories.

动态集成可视化支持对不确定轨迹的理解。

• DOI: 10.1037/xap0000370
• 发表时间: 2021
• 期刊: Journal of Experimental Psychology: Applied
• 作者: Witt, Jessica K.;Clegg, Benjamin A.
• 通讯作者: Clegg, Benjamin A.

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

To Vaccinate or Not? The Role Played by Uncertainty Communication on Public Understanding and Behavior Regarding COVID-19

• DOI: 10.1177/10755470211063628
• 发表时间: 2021-12-27
• 期刊: SCIENCE COMMUNICATION
• 影响因子: 9
• 作者: Kelp, Nicole C.;Witt, Jessica K.;Sivakumar, Gayathri
• 通讯作者: Sivakumar, Gayathri

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

Tool Use Affects Spatial Perception

工具使用影响空间感知

• DOI: 10.1111/tops.12563
• 发表时间: 2021
• 期刊: Topics in Cognitive Science
• 影响因子: 3
• 作者: Witt, Jessica K.