Leveraging Crowdsourced Data to Assess Spatiotemporal Patterns of Resilience in Diverse Gulf Coast Communities Impacted by Natural Hazards

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The severity and cost of flood events continue to increase in the US, often with disproportionate impacts on vulnerable populations who may have higher sensitivity to the negative effects of flooding and lower capacity to adapt. Understanding what makes communities vulnerable or resilient to flooding is critical to developing mitigation actions that can reduce the negative effects of future hazards. However, existing frameworks for assessing resilience often fall short, as they do not address important dynamic and highly localized factors that influence peoples’ ability to cope with, adapt to, and recover from natural hazards. This Disaster Resilience Research Grants (DRRG) project will develop a bottom-up, community-driven framework for local-level resilience assessment by generating and utilizing high-resolution crowdsourced datasets and leveraging local knowledge and experiences to examine how the factors contributing to resilience (i.e., exposure, sensitivity, and adaptive capacity) vary over space and time. Findings will have implications for more effective resilience building. As part of the project, crowdsourcing efforts using Streetwyze, a community-driven mapping platform, will increase the awareness of flooding and its daily impacts in communities and will encourage diverse voices to participate in the collection of data to support local resilience planning efforts.This project will use a mixed-methods, sociotechnical approach to examine how crowdsourced datasets can be leveraged to (1) improve the spatiotemporal characterization of factors that influence community resilience to flood disasters, (2) develop new metrics that account for the dynamic social and physical nature of resilience, and (3) encourage more equitable capacity-building to reduce the impacts of future floods and enhance disaster resilience across diverse populations. The project focuses on flood hazards in coastal Mississippi and engages with diverse community partners from flood-prone areas in the cities of Gulfport and Biloxi. Two novel crowdsourcing technologies that include passively collected mobility data and actively generated qualitative and imagery data will be used to characterize fine-scale spatial and temporal patterns of exposure, sensitivity, and adaptive capacity. Surveys will evaluate how community members use the crowdsourced data and will assess the role of demographic and socioeconomic factors in influencing participation in the crowdsourcing effort. Geospatial, statistical, and machine learning models will be developed and applied to integrate the crowdsourced datasets with conventional sensor, satellite, and survey data. Model outputs will be used to develop novel measurement approaches that improve assessments of the factors contributing to community resilience.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.