Citizen Science EAGER: Quantifying Uncertainty in Crowd Response for Reliable Wind Hazard and Damage Assessment

公民科学 EAGER：量化人群反应的不确定性，以进行可靠的风灾和损害评估

• 批准号: 1645386
• 负责人: Hadi Meidani
• 金额: $ 10万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1645386&HistoricalAwards=false
• 关键词: Citizen; Science; EAGER; Quantifying; Uncertainty

Damage to infrastructure arising from windstorms exceeds damage from any other natural hazard in the U.S. The highly variable nature of wind loadings on buildings during a windstorm, however, means that accurate characterization at the damage location may not be captured by current measurement networks. Ubiquitous smartphone and internet availability, widespread use and rapid dissemination of social media, the power of crowds engaged in scientific endeavors, and the public's awareness of vulnerabilities point to a paradigm shift in sensing hazards in general. In the case of windstorm damage, on-the-ground data retrieved and shared by Citizen Science public participation may provide windstorm data previously unavailable. The primary focus of this EArly-concept Grant for Exploratory Research (EAGER) award is to study "human-sensor" data collected through Amazon Mechanical Turk-- a crowd sourcing application. Volunteers will be shown images and related data from actual windstorms and asked to characterize the damage and their confidence in their assessments. These data will be used to design a crowd sourcing algorithm that will enable robust Citizen Science public participation in the rapid identification of damage areas to help decision makers to allocate resources for damage response and recovery efforts and for targeted damage assessments, which can help to improve the design of buildings in regions susceptible to intense windstorms. This project will address two key questions: How can one quantify the confidence in crowdsourced damage assessment? How can one design a tool for more reliable crowdsourcing given unreliable participants? Researchers will initially compile a "validation set" of data that includes imagery, damage states and wind speed estimates for approximately 8,000 structures that were affected by the Joplin, MO tornado. The data set will be used to create a reliable crowdsourced image classification scheme in the form of online questionnaires for the public. The questionnaires will be tested by collecting assessment reports from participants on Amazon Mechanical Turk. These reports will inform development of an uncertainty model for participant reliability, which forms the basis for a coding-theoretic crowdsourcing algorithm that is robust to uncertainties due to unreliable participants. This algorithm will be tested against a separate dataset to compare the researchers' approach with one that doesn't control for participant unreliability. The final research results will be shared with NOAA for use in training surveyors to assess wind damage and to provide tutorials for the public.

风暴引起的基础设施的损害超过了美国任何其他自然危害的损害，但在风暴期间，建筑物上风负载的高度可变性质，这意味着当前测量网络可能不会捕获损害位置的准确表征。无处不在的智能手机和互联网可用性，社交媒体的广泛使用和快速传播，从事科学努力的人群的力量以及公众对脆弱性的意识表明，一般情况下，感知危害的范式转变。 在暴风雨破坏的情况下，公民科学公众参与检索和共享的地面数据可能会提供以前无法使用的风暴数据。这项早期概念研究授予探索性研究奖的主要重点是研究通过Amazon Mechanical Turk收集的“人类传感器”数据，这是一种人群采购应用。 将向志愿者展示来自实际暴风雨的图像和相关数据，并要求表征他们对评估的损害和信心。 这些数据将用于设计人群采购算法，该算法将使强大的公民科学公众参与快速识别损害区域，以帮助决策者分配资源以进行损害响应和恢复工作以及有针对性的损害评估，这可以帮助改善容易受到强烈风风暴的地区建筑物的设计。 该项目将解决两个关键问题：一个人如何量化对众包损害评估的信心？ 一个人如何设计工具，以使参与者更可靠的众包？研究人员最初将编译一个“验证”数据集，其中包括图像，损害状态和风速估算，该数据受约8,000个受乔普林Mo Tornado影响的结构的估计。数据集将用于以公众的在线调查表的形式创建可靠的众包图像分类方案。问卷将通过收集亚马逊机械土耳其人的参与者的评估报告来测试。这些报告将为参与者可靠性的不确定性模型开发，这是编码理论众包算法的基础，由于参与者不可靠，对不确定性是可靠的。该算法将针对单独的数据集进行测试，以将研究人员的方法与无法控制参与者不可靠的方法进行测试。最终的研究结果将与NOAA共享，以用于培训测量师，以评估风能损害并为公众提供教程。