Near Real-Time Decision Support for Disaster Management: Hurricane Sandy

Abstract

Assessing vulnerability is fundamental for efficient risk management and emergency response. Integrating analyses from preparedness and risk reduction to inform the response phase requires that structural information about demographics or industry is combined with specific local information that highlights hotspots or emerging risks in near real-time. Owing to its availability on social media or other platforms, this local information is today often collected and processed remotely with the aim to inform responders and the public via reports, maps and apps published online. This paper addresses and discusses the challenges of remote near-real time vulnerability assessments by using an indicator model, which enables the combination of heterogeneous types of information while keeping track of the associated uncertainty. This approach is illustrated by the near real-time assessments for Hurricane Sandy that hit the East Coast of the United States in 2012. © Springer International Publishing Switzerland 2014.