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Project Overview: This NSF-funded project (Award #2019754) is part of the Belmont Forum’s Disaster Risk, Reduction, and Resilience (DR3) initiative, a global effort to assess and mitigate disaster risks through transdisciplinary collaboration. The study investigates strategies to enhance the resilience of low-income communities living in flood-prone and climate-vulnerable regions, with a geographic focus on Brazil, East Africa, and the southeastern United States. The U.S. component centers on coastal and urban communities in Florida, particularly those at risk from flooding and extreme weather events. Research Objectives: Through a transdisciplinary approach, the project integrates machine learning, geospatial analytics, and socio-economic data to: - Assess community-level vulnerabilities to flooding and extreme heat, -Identify barriers to adopting disaster-resilient housing, - Co-design affordable, climate-resilient housing prototypes using sustainable, locally sourced materials. The research aims to support community-informed design strategies and policy recommendations that are adaptable across different socio-economic and geographic contexts. Dataset Description: The dataset contains responses from approximately 500 residents aged 18+ living in low-income, flood-prone neighborhoods in Florida. The survey captures detailed information on: - Housing conditions and infrastructure, - Disaster preparedness and flood risk perception, - Access to services during and after disasters, - Health and economic impacts of extreme weather events, - Community cohesion and recovery strategies. This dataset serves as a resource for researchers, urban planners, emergency response agencies, and policymakers seeking data-driven insights to inform resilient housing design, climate adaptation, and disaster recovery planning. Data Collection and Anonymity: Survey distribution and data collection were conducted in partnership with Centiment, a third-party research company that recruits demographically targeted panels for academic and applied research. For this study, Centiment distributed the survey to residents of low-income, flood-prone communities in Florida, based on geographic and socio-economic criteria specified by the research team. All personally identifiable information (PII), such as IP addresses, email addresses, and precise geolocation data, was removed prior to uploading the dataset to DesignSafe. The dataset has been reviewed to ensure participant anonymity in accordance with DesignSafe data protection policies and applicable ethical standards. 

Description: This NSF-funded project (Award #2135713) investigates resilience in low-income households by examining energy efficiency, appliance use, and adaptive mechanisms among marginalized communities, with a focus on Pennsylvania's urban centers. The research aims to establish pathways to disaster-resilient, healthy, and sustainable cities, where the voices and agency of vulnerable populations are prioritized. Central objectives include identifying resilience capacities, well-being outcomes, and existing adaptive management mechanisms, especially concerning access to efficient appliances and alternative energy sources for low-income households during extreme weather events. Data Reuse: This dataset, which spans three rounds of data collection (May 2023, May 2024, and September 2024) targeting the low-income communities in Pennsylvania, is valuable for comparison with U.S. Energy Information Administration (EIA) data and includes questions similar to those from the Residential Energy Consumption Survey (RECS) for comparability. This dataset can be used to investigate household appliance usage, resilience during extreme temperatures, and adoption rates of energy-efficient appliances. The dataset can further inform policies on energy access and efficiency in low-income settings, especially as they pertain to marginalized urban communities. Uniqueness: Unlike national datasets, this research integrates community voices and focuses on marginalized, low-income urban populations, revealing their unique energy resilience challenges. Additionally, the study’s structure—collecting data across multiple seasons—enables a nuanced analysis of seasonal influences on energy resilience and appliance use. Audience: This dataset will benefit academic researchers in sustainability, policymakers, and public health advocates interested in disaster resilience and low-income energy access. It is also intended for use by local community organizations that support sustainable urban development and resilience-building for vulnerable populations.