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Flash flooding events, with their intense and sudden nature, present unique challenges for disaster researchers and emergency planners. To quantify the extent to which hotspots of flash flooding share similar social and physical features, the research uses community scale crowdsourced data and k means clustering. Crowdsourced data offers the potential to allocate limited resources, to improve spatial understanding, and to minimize the future effects of natural hazards. The research evaluates the impacts of Tropical Storm Imelda on Houston Metropolitan and Hurricane Ida on New York City. It develops a combined flash flood impact index based on FEMA claims, 311 calls, and Waze traffic reports which is able to capture a combination of crowdsourced data for the societal impact of flash flooding. In addition, k means clustering offers an essential tool for evaluating attributes associated with flash flooding events by grouping data into k number clusters of features. Thus, k means clustering evaluates the significance of a community's socio demographic, social capital, and physical features to the combined flood impact index. To ensure accessibility and replicability to different types of communities, our research uses publicly available datasets to understand how socio demographic data, social capital, and physical connectivity and development affect flash flood resilience. The findings reveal the intricate relationships associated with flash flooding impact as a combination of socio demographic and physical features. For instance, the cluster with the highest scores of the flash flood impact index from Tropical Storm Imelda had the highest percentage of minority population and lower income while the cluster with the second highest score flash flood impact index had the highest percentage of impervious surface and number of POIs.
Clarke et al. (Mon,) studied this question.