Mining Flood Insurance Big Data to Reveal the Determinants of Humans' Flood Resilience

Human behavior has shown to have a significant impact on future flood risk. The state-of-the-art research regarding human behavior before, during and after flood events is predominantly based on site- and event-specific survey data or psychological theories. In recent years, the availability of large-scale databases provides an empirical basis for dynamical approaches to model the impacts of heterogeneous individual and societal behavioral patterns of flood risk. The US Federal Emergency Management Agency has recently released household-scale data on national flood insurance policies in-force since 2009, covering the whole US. Providing access to flood insurance is an effective strategy to increase resilience by enabling inhabitants in flood prone areas and their property to quickly recover from flood events. In this work, we analyze flood insurance purchase, considered as a proxy of flood awareness and preparedness, by data mining techniques, spatially correlating and modeling insurance ratios and socioeconomic data in official floodplains. Recent or regular exposure to flood events has shown to be another important factor influencing flood risk perception, in addition to socio-economic variables. Therefore, the effect of flood experience on flood insurance uptake is analyzed. This study ultimately contributes a data-driven approach to identify the main determinants and dynamics of flood insurance purchase throughout different states and social backgrounds. Understanding the factors driving people’s choices regarding flood insurance purchase is the first step to improve the National Flood Insurance Program's strategies and address societal inequalities in disaster risk management.