Data Driven Disaster Planning in New York, Tokyo, and Taipei

DESCRIPTION: By 2050, 70% of the world's population will live in urban areas. As cities continue to grow, disaster risk is expected to increase exponentially. Our proposed research aims to design a replicable and transferrable system for disaster preparedness and response in megacities. Such a system would integrate an understanding of natural hazards as well as human behavior in order to inform immediate response in the face of rapid-onset disasters, such as floods, earthquakes, and fires. Furthermore, this research will aid critical decisions regarding long-term urban resilience. Case studies selected for this project are New York City due to their complex urban environments, intense hazards (i.e., floods and hurricanes), and diverse populations, which present a challenge for disaster management. We will consider how to apply our strategies developed for NYC's floods and hurricane to Tokyo and Taipei, which faces different hazards but faces similar urban challenges. Traditionally, disaster planning has relied on a limited analysis of possible disaster scenarios. Notably, past planning efforts often do not distinguish between event time of day, workdays vs. weekends, seasons, or urban locations (e.g., indoor, outdoor, underground). Furthermore, traditional approaches have failed to capture the diverse needs of the affected social groups. For example, early warning systems near Tokyo Station have not been provided in foreign languages or in a friendly manner for the disabled and elderly. More broadly, a limited scenario analysis has serious implications for urban resilience. Current infrastructure systems tend not to be designed to withstand mega-disasters. Here, we propose to address dynamic disaster scenarios as well as the needs of vulnerable socio-economic groups in urban areas. The research has three key components: 1) data collection, analysis, and simulation of hazards and human responses, 2) design of information-sharing systems and emergency response, and 3) policy recommendations for urban risk governance towards resilience by developing a framework to collaborate with stakeholders and analyzing vulnerability across socio economic groups.

The underlying hypothesis is that the integration of dynamic data sources during disasters, and information about the vulnerability of social groups into a scenario and data-driven approaches can significantly improve disaster planning. We will develop and improve methods for dynamic updating of disaster conditions and scenarios; link them to existing information-sharing systems; apply developed methods to other megacities in the world; and make recommendations for urban risk governance to respond to dynamic situations of disasters to address the vulnerability of social groups in urban areas.