Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response

基础设施是文明的基石。地震（如海地、日本）、飓风（如卡特里娜、桑迪）或洪水（如克什米尔洪水）等自然灾害对基础设施造成的破坏可能导致重大的经济损失和社会苦难。人员协调和信息交流是损害控制的核心。该项目旨在通过整合社会、物理和危险模型，从根本上改革决策支持系统，以管理迅速变化的灾害情况。该研究团队将成为计算机科学、工程、自然科学和社会科学研究人员之间高度整合和协作工作的典范，用于本科生和研究生的研究、教育和培训，包括那些来自代表性不足群体的学生。该团队试图设计新颖的、多维的、跨模态的聚合和推理方法，以补偿受影响地区传感模式覆盖范围的不均匀。他们将来自社会和物理传感器的数据输入到一个综合模型中，并以此设计一种新的方法来预测和优先考虑损害的后果；它们包括对人、民用基础设施（交通、电力、水路）及其组成部分（如桥梁、交通信号）造成的损害在时间上和概念上的延伸后果。他们正在开发创新技术，以支持新的背景知识和结构化数据的识别，以改进对象提取、位置识别相关性以及跨多个来源和模式（社会、物理和网络）的相关数据集成。他们使用社会语言和网络分析的新耦合来识别重要的人和物体，关于交通和运输网络的统计和事实知识，以及对危害模型（例如风暴潮）和洪水制图的影响。他们正在开发基于领域的机制，以解决普遍存在的可信度和可靠性问题。示范成果包括为第一响应者和恢复团队提供的特定工具，以帮助确定救援和修复工作的优先次序，以及改进洪水响应、城市制图和动态风暴潮模型。他们还为学生提供跨学科培训，结合俄亥俄州立大学新开设的数据分析本科专业和赖特州立大学的大数据和智能数据研究生证书课程，利用教育学研究。

Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g., Haiti, Japan), a hurricane (e.g., Katrina, Sandy), or a flood (e.g., Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project aims to radically reform decision support systems for managing rapidly changing disaster situations by the integration of social, physical and hazard models. The researcher team will serve as a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences, and the social sciences for research, education, and training of undergraduate and graduate students, including those from under-represented groups.The team seeks to design novel, multi-dimensional, cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. They use data from social and physical sensors as input into an integrated model, from which they are designing a new methodology to predict and prioritize the consequences of damage; they are including both temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g., bridges, traffic signals). They are developing innovative technology to support the identification of new background knowledge and structured data to improve object extraction, location identification correlation, and integration of relevant data across multiple sources and modalities (social, physical and Web). They use novel coupling of socio-linguistic and network analysis to identify important persons and objects, statistical and factual knowledge about traffic and transportation networks, and the resulting impact on hazard models (e.g. storm surge) and flood mapping. They are developing domain-grounded mechanisms to address pervasive trustworthiness and reliability concerns. Exemplar outcomes include specific tools for first-responders and recovery teams to aid in the prioritization of relief and repair efforts as well as improved flood response, urban mapping, and dynamic storm surge models. They also are providing interdisciplinary training of students, leveraging research in pedagogy in conjunction with Ohio State University's new undergraduate major in data analytics and Wright State University's Big and Smart Data graduate certificate program.