Integrated Stage-based Evacuation with Social Perception Analysis and Dynamic Population Estimation

灾难事件期间的有效疏散是美国许多地方政府机构面临的最具挑战性的问题之一。该研究项目将开发一个集成野火疏散决策支持系统的原型，并创建分析工具，将与疏散规划者和应急资源经理一起进行评估。我们的跨学科研究团队将与圣地亚哥县紧急服务办公室(OES)、美国红十字会圣地亚哥/帝国县分会和2-1-1圣地亚哥合作开发这个基于网络的系统。这项研究将帮助应急机构更好地了解公众在灾难性事件中的感知和需求，并为当地社区制定更有效的疏散计划。研究框架可以扩展到其他类型的自然灾害(例如海啸、飓风、洪水灾害)，但需要进行一些修改，以满足疏散计划的不同需要。本项目开发的动态人口密度模型可应用于城市规划、选举、商业营销、设施管理等领域。社会感知分析模型和舆情监测器可以帮助其他研究领域，如交通事件检测和公众运动。该项目最有价值的组成部分之一是通过使用移动电话应用程序和在线论坛将登记的当地志愿人员联系起来，建立一个驻地反馈网络。该项目还将包括研究生的参与，通过各种论坛进行传播，包括一个项目网站和一个多学科研究人员参与的论坛。该项目将组织三次夏季研讨会，以促进研究人员和政府机构之间未来的多学科合作。该项目将利用大数据驱动的技术，整合包括社交媒体、人口普查、地理信息系统(GIS)数据层、志愿者建议和遥感数据在内的多种数据源，以开发一个集成的野火疏散决策支持系统(IWEDSS)。该系统将为数据采集、交通需求建模、疏散作业和信息发布提供关键功能。它将为疏散规划者和政府机构提供基于科学和数据驱动的分析工具，以做出更好的决策，从而减少疏散时间和潜在的伤亡人数。该项目的四个主要目标是：(1)通过集成多种数据源和地理信息系统模型，建立城市地区动态估计的人口分布(密度)模型；(2)根据人口密度分布设计分阶段的疏散计划，并开发稳健的优化模型，以考虑需求的不确定性；(3)创建舆情监测器和居民反馈网络，通过实时分析社交媒体和志愿者建议，了解当地社区对灾害的社会感知，从而改进疏散计划；(4)建立基于网络的地理空间分析平台，为决策者、应急资源管理者和公职人员提供交互式决策支持工具。这个跨学科的项目将依赖于融合地理信息科学、地图学、土木工程、交通运输和社交媒体分析，以促进将传统的静态疏散规划程序转变为动态的、以用户为中心的、易于使用的和数据驱动的空间决策支持系统。

Effective evacuation during disastrous events is one of the most challenging issues for many local government agencies in U.S. This research project will develop a prototype integrated wildfire evacuation decision support system and create analytic tools that will be evaluated with evacuation planers and emergency resource managers. Our interdisciplinary research team will collaborate with the Office of Emergency Services (OES) of San Diego County, the San Diego/Imperial Counties Chapter of the American Red Cross, and 2-1-1 San Diego to develop this web-based system. This research will help emergency response agencies better understand public perceptions and needs during disastrous events, and create more effective evacuation plans for local communities. The research framework can be extended to other types of natural disasters (e.g., tsunami, hurricanes, flood hazards) with some modifications to cope with different needs of evacuation plans. The dynamic population density model developed in this project can be applied in urban planning, elections, business marketing, and facility management. The social perception analysis model and public opinion monitors can help other research domains such as traffic incident detection and public campaigns. One of the most valuable components in this project is the establishment of a resident feedback network by connecting registered local volunteers using a mobile phone application and an online forum. The project will also include involvement of graduate students, dissemination through various fora, including a project website and a discussion forum to involve multidisciplinary researchers. Three summer workshop meetings will be organized to facilitate future multidisciplinary collaborations among researchers and government agencies.Using Big Data-driven techniques, this project will integrate multiple data sources including social media, census survey, geographic information systems (GIS) data layers, volunteer suggestions, and remote sensing data to develop an integrated wildfire evacuation decision support system (IWEDSS). This system will provide key functions for data collection, traffic demand modeling, evacuation operation, and information dissemination. It will offer scientifically-based and data-driven analytic tools for evacuation planers and government agencies to make better decisions that can reduce the evacuation time and potential number of injuries and deaths. The four main goals of this project are to (1) build a dynamic estimated population distribution (density) model in urban areas by integrating multiple data sources and GIS models; (2) design stage-based evacuation plans with population density distributions and develop robust optimization models to account for demand uncertainties; (3) create a public opinion monitor and a resident feedback network to improve evacuation plans by understanding social perception of the disasters in local communities through the real-time analysis of social media and volunteer suggestions; (4) build a web-based geospatial analytics platform and provide interactive decision support tools for decision makers, emergency resource managers, and public officers. This interdisciplinary project will rely upon a convergence among GIScience, cartography, civil engineering, transportation, and social media analytics to facilitate the transformation of traditional static evacuation planning procedures into a dynamic, user-centered, easy-to-use, and data-driven spatial decision support system.