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

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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.
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