An official website of the United States government
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.
more »
« less
NSF BRITE Synergy: Developing and Validating a Framework for Measuring Resilience in Low-Income Housing in the Post-Pandemic World
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.
more »
« less
- Award ID(s):
- 2135713
- PAR ID:
- 10563063
- Publisher / Repository:
- Designsafe-CI
- Date Published:
- Subject(s) / Keyword(s):
- low income communities extreme weather energy poverty community resilience
- Format(s):
- Medium: X
- Institution:
- The Pennsylvania State University
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Assisting households with maintaining adequate energy supply is one method for improving overall quality of life. Households experiencing energy insecurity may be unable to afford to use energy for necessary services at home (e.g., unable to purchase air conditioners). Energy efficiency (EE) can reduce energy costs for low-income households–requiring less energy for essential activities. While existing research has identified the groups that are less likely to participate in energy efficiency programs, there is limited research on how participation impacts energy insecurity among vulnerable households when they participate. Using over 138,000 households in Tallahassee, Florida we study participants in a neighborhood program that targeted underserved communities. We conduct quasi-experimental difference-in-difference comparisons for seasonal energy consumption, energy bills, and energy burden during the cooling season in response to air conditioning (AC) appliance purchases. We compare impacts for households in the program (REACH) and higher income non-REACH qualified households. We find that REACH homes, on average, save approximately 300kWh-eq on energy or $25 seasonally after purchasing an AC unit. While AC rebates reduce seasonal energy burden by 0.6 % in non-REACH homes, there is no statistically significant change in seasonal energy burden for REACH homes. The difference in energy reduction between REACH and non-REACH qualified homes could be due to increases in AC use among REACH homes after rebates. Further work could explore this trend of potential increases in efficient appliance use among low-income homes.more » « less
-
Residential buildings constitute roughly one-fourth of the total energy use across the globe. Numerous studies have shown that providing an energy breakdown increases residents' awareness of energy use and can help save up to 15% energy. A significant amount of prior work has looked into source-separation techniques collectively called non-intrusive load monitoring (NILM), and most prior NILM research has leveraged high-frequency household aggregate data for energy breakdown. However, in practice most smart meters only sample hourly or once every 15 minutes, and existing NILM techniques show poor performance at such a low sampling rate. In this paper, we propose a TreeCNN model for energy breakdown on low frequency data. There are three key insights behind the design of our model: i) households consume energy with regular temporal patterns, which can be well captured by filters learned in CNNs; ii) tree structure isolates the pattern learning of each appliance that helps avoid magnitude variance problem, while preserves relationship among appliances; iii) tree structure enables the separation of known appliance from unknown ones, which de-noises the input time series for better appliance-level reconstruction. Our TreeCNN model outperformed seven existing baselines on a public benchmark dataset with lower estimation error and higher accuracy on detecting the active states of appliances.more » « less
-
Unplanned disaster events can greatly disrupt access to essential resources, with calamitous outcomes for already vulnerable households. This is particularly challenging when concurrent extreme events affect both the ability of households to travel and the functioning of traditional transportation networks that supply resources. This paper examines the use of volunteer-based crowdsourced food delivery as a community resilience tactic to improve food accessibility during overlapping disruptions with lasting effects, such as the COVID-19 pandemic and climate disasters. The study uses large-scale spatio-temporal data (n = 28,512) on crowdsourced food deliveries in Houston, TX, spanning from 2020 through 2022, merged with data on community demographics and significant disruptive events occurring in the two-year timespan. Three research lenses are applied to understand the effectiveness of crowdsourced food delivery programs for food access recovery: 1) geographic analysis illustrates hot spots of demand and impacts of disasters on requests for food assistance within the study area; 2) linear spatio-temporal modeling identifies a distinction between shelter-in-place emergencies and evacuation emergencies regarding demand for food assistance; 3) structural equation modeling identifies socially vulnerable identity clusters that impact requests for food assistance. The findings from the study suggest that volunteerbased crowdsourced food delivery adds to the resilience of food insecure communities, supporting its effectiveness in serving its intended populations. The paper contributes to the literature by illustrating how resilience is a function of time and space, and that similarly, there is value in a dynamic representation of community vulnerability. The results point to a new approach to resource recovery following disaster events by shifting the burden of transportation from resource-seekers and traditional transportation systems to home delivery by a crowdsourced volunteer network.more » « less
-
Abstract Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States.more » « less
