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1. Tracking the post-disaster evolution of water infrastructure resilience: A study of the 2021 Texas winter storm

   https://doi.org/10.1016/j.scs.2023.104417  

   Tiedmann, Helena R.; Spearing, Lauryn A.; Castellanos, Sergio; Stephens, Keri K.; Sela, Lina; Faust, Kasey M. (January 2023, Sustainable Cities and Society)

   In February 2021, severe winter weather in Texas caused widespread electrical blackouts, water outages, and boil water notices. Water systems faced extensive challenges due to cascading failures across multiple interde- pendent infrastructure systems. Water utilities have since made considerable progress in improving resilience to extreme events, but ongoing challenges remain. Through a qualitative analysis of semi-structured interviews with 20 large water utilities in Texas, this study tracks the evolution of water infrastructure resilience across three phases: the storm and immediate aftermath, the subsequent one-year period, and the “new normal” in the post-disaster environment. We consider five dimensions of resilience—economic, environmental, governance, infrastructure, and social—to identify where solutions have been implemented and where barriers remain. This study contributes to efforts throughout the United States to build more robust water systems by capturing lessons learned from Winter Storm Uri and providing recommendations to improve hazard preparedness, resilience, and public health. 

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2. Resource-sharing behavior while sheltering-in-place: A latent class analysis to guide community-based relief distribution

   https://doi.org/10.1016/j.ijdrr.2025.105532  

   Chou, Chi-Ya; Borowski, Elisa; Stathopoulos, Amanda (July 2025, International Journal of Disaster Risk Reduction)

   Building community resilience is vital due to climate change and more frequent extreme weather events, which often force people to choose between evacuating or sheltering in place. The prevalence of stay-at-home orders and quarantine practices emerging from the COVID-19 pandemic highlights the need to understand how households access resources when mobility is restricted. This research investigates peer-to-peer resource-exchanging behavior during a shelterin- place response to a flooding event amid the pandemic through an online stated response survey (n=600). Latent class analysis reveals six distinct segments based on respondents’ resource sharing and accepting behaviors. Several household and social context variables help explain these behavioral clusters. Younger individuals and individuals with lower household income are generally more reluctant to accept resources from neighbors, while larger households are more inclined to share essential items. Additionally, social resources, trust in neighbors, and preparedness level can significantly influence individuals’ resource-exchanging behaviors. The findings highlight gaps for governmental agencies and nonprofit organizations to help address, emphasizing the need to ensure sufficient allocation of resources, especially for private items such as backup power sources, communication devices, and shelter, which respondents are least willing to share. This research offers valuable insights for future disaster preparedness programs and resource allocation strategies, aiming to improve community resilience and minimize negative impacts during shelter-in-place responses. 

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3. NSF Belmont Forum Collaborative Research: Advancing Resilience in Low-Income Housing Using Climate-Change Science and Big Data Analytics

   https://doi.org/10.17603/ds2-1kpb-dv70  

   Obonyo, Esther; Mutunga, James; Mirhosseini, Homeira (January 2025, Designsafe-CI)

   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. 

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4. Factors Influencing Willingness to Share Resources Postdisaster: A Cross-Cultural Comparison between US and Japanese Communities

   https://doi.org/10.1061/NHREFO.NHENG-1836  

   Idziorek, Katherine; Abramson, Daniel B; Kitagawa, Natsuki; Yamamoto, Toshiyuki; Chen, Cynthia (November 2023, Natural Hazards Review)

   When large-scale disasters occur, people often are left on their own to seek critical resources: food, water, medications, and other important items. Historically, government agencies have developed disaster preparedness strategies focused primarily on either the level of the individual or household or on the ability of government agents to provide relief to affected areas. Such approaches do not consider the potential for community members to share needed resources with one another—a crucial factor in survival when earthquakes, floods, landslides, and other disruptions to transportation and communications cut off whole communities from external aid. In this study, we used a simple random sample survey to measure households’ actual and perceived preparedness and assess individuals’ willingness to share essential resources following a large disaster using survey data gathered from three communities in the Pacific Northwest of the US (N 1⁄4 638; overall response rate 20.1%) and Nagoya, Japan (N 1⁄4 1,043; response rate 13.6%), two regions that expect to experience a magnitude 9.0 megaquake. Analysis of the survey data using an ordered response probit model found that the strength of social ties and levels of social trust strongly influence willingness to share in both regions. Differences between the Japanese and American responses suggest different dependencies on and roles for government agencies in the two societies, as well as differences in the types of resources that community members are willing to share, and with whom. Trust emerges as the most important factor across both study regions and for all resources. Willingness to share may be enhanced through trust-building interventions and should be regarded as an effective focus for preparedness efforts, especially if it is shown to be beneficial for a variety of social purposes. 

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5. Building occupancy type classification and uncertainty estimation using machine learning and open data

   https://doi.org/10.1017/eds.2025.2  

   Narock, Tom; Johnson, J Michael; Singh-Mohudpur, Justin; Modaresi_Rad, Arash (January 2025, Environmental Data Science)

   Abstract Federal and local agencies have identified a need to create building databases to help ensure that critical infrastructure and residential buildings are accounted for in disaster preparedness and to aid the decision-making processes in subsequent recovery efforts. To respond effectively, we need to understand the built environment—where people live, work, and the critical infrastructure they rely on. Yet, a major discrepancy exists in the way data about buildings are collected across the United SStates There is no harmonization in what data are recorded by city, county, or state governments, let alone at the national scale. We demonstrate how existing open-source datasets can be spatially integrated and subsequently used as training for machine learning (ML) models to predict building occupancy type, a major component needed for disaster preparedness and decision -making. Multiple ML algorithms are compared. We address strategies to handle significant class imbalance and introduce Bayesian neural networks to handle prediction uncertainty. The 100-year flood in North Carolina is provided as a practical application in disaster preparedness. 

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