BDD: Human-Centered Situational Awareness Platform for Disaster Response and Recovery

BDD：以人为本的灾难响应和恢复态势感知平台

• 批准号: 1461963
• 负责人: Cyrus Shahabi
• 金额: $ 30万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1461963&HistoricalAwards=false
• 关键词: BDD; Human; Centered; Situational; Awareness

Efficient and thorough data collection and its timely analysis are critical to any disaster response and recovery system in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are major data science challenges. Moreover, the effective presentation of the collected knowledge to human decision-makers is an open problem. In this project, these challenges of data collection, analysis and presentation are collectively referred to as 'Situational Awareness', and are studied by a collaborative team of data scientists from the University of Southern California (USC) in United States and the National Institute of Informatics (NII) in Japan.The first objective of this project is to devise effective techniques for comprehensive data collection. The challenge is that during disaster the availability of information is spatially biased and some areas are not well covered by available information. Towards this end, USC's spatial crowdsourcing platform, dubbed MediaQ, is utilized to collect pictures and videos on-demand from mobile devices of people in the vicinity of the disaster areas to facilitate effective collection, orchestration and aggregation of information. The second objective is to develop efficient methods for organizing and analyzing incoming data streams for human decision makers, given the challenges of volume and variety of unstructured data whose veracity is unknown. The collaborative US-Japan team takes advantage of their combined expertise in developing Spatial-Temporal-Thematic analytics engines and Geospatial Image Filtering Tools to meet this objective. Finally, for an effective presentation of the knowledge to human stakeholders, NII's DiVE virtual environment engine is used to facilitate seamless presentation and communication for both onsite (humans in the field) and offsite (disaster managers at the center) stakeholders.

有效而彻底的数据收集及其及时分析对于任何灾难响应和恢复系统至关重要，以便在灾难中挽救人们的生命。但是，在灾害区域中获取综合数据及其快速分析以将数据转换为可行的知识是主要数据科学挑战。此外，将收集的知识的有效呈现给人类决策者是一个开放的问题。 在该项目中，这些数据收集，分析和演示的这些挑战共同称为“情境意识”，由美国南加州大学（USC）的数据科学家协作团队和日本国家信息学院（NII）的协作团队进行了研究。该项目的第一个目标是为有效的技术收集。面临的挑战是，在灾难期间，信息的可用性在空间上是偏见的，并且有些区域没有被可用的信息涵盖。为此，USC的空间众包平台（称为Mediaq）被用来从灾难区附近的移动设备上收集图片和视频，以促进有效的收集，编排和信息聚合。第二个目标是开发有效的方法来组织和分析人类决策者的传入数据流，鉴于数量和多种非结构化数据的挑战，其真实性未知。美国日本协作团队利用了他们在开发空间 - 周期性分析引擎和地理空间图像过滤工具方面的综合专业知识来满足这一目标。最后，为了有效地向人类利益相关者展示知识，NII的潜水虚拟环境引擎用于促进无缝的表现和沟通，并为现场（现场的人类）和异地（中心的灾难经理）利益相关者提供了无缝的介绍和沟通。