This National Science Foundation (NSF) Project Grant award under the Engineering program (CFDA 47.041) supports research to improve disaster preparedness and response strategies by leveraging advances in language models and human mobility data. The $499,929 award, spanning June 2024 to May 2027, aims to: 1) develop advanced GPT models to predict human mobility behavior under various disaster scenarios using large-scale mobile data, and 2) create a "Global Data Commons for Disaster...
This $400,000 project grant was awarded by the National Science Foundation (NSF) under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program. The project examines the impacts of natural disasters on community resilience, focusing on how disasters disrupt human mobility patterns, aid requests, and voluntary support networks. The research involves analyzing geospatial data from mobile devices, social media, and government records to understand spatial disparities in disaster response...
This National Science Foundation (NSF) project grant, awarded under the Engineering program (CFDA 47.041), provides $399,999 to North Dakota State University (NDSU) to advance research and methods for improving disaster resilience and recovery among minority- and women-owned small and medium-sized enterprises (SMEs). The project aims to: 1) examine if accelerating resilience investments is more effective than reducing recovery duration; 2) explore differences in resilience and recovery between...
This National Science Foundation (NSF) Engineering (47.041) Project Grant of $396,200 was awarded to The University of Texas at Arlington on November 1, 2021 to support research through October 31, 2023. The grant aims to leverage crowdsourced data to assess spatiotemporal patterns of resilience in diverse Gulf Coast communities impacted by natural hazards. Products and services will include analysis of how crowdsourced data can measure community resilience following disasters. A sub-award of...
This National Science Foundation (NSF) Engineering (CFDA 47.041) Project Grant award in the amount of $454,236 to the University of Massachusetts Lowell will advance the field of resilience analytics and enhance the ability of civil infrastructure systems to withstand natural disasters. The key research objectives include: (i) Adapting statistical physics and discrete modeling techniques to predict the type, extent, and functional integrity of damage to structural and non-structural building...
The National Science Foundation (NSF) awarded a $1,500,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of North Texas Research and Innovation Division. The grant funding aims to develop a comprehensive framework to streamline diverse processes and integrate advanced technological solutions into disaster relief operations, enhancing the overall effectiveness and speed of emergency response efforts. The key objectives are to...
This $165,000 Project Grant award from the National Science Foundation (NSF) Engineering (CFDA 47.041) program supports the development of a novel adaptive data collection framework for automated post-disaster rapid damage assessment. The project at Arizona State University's Orspa division will create a Bayesian-based system that analyzes recent observations to dynamically update the trajectories of data collector agents toward areas with the greatest potential for information gain. This will...
The National Science Foundation (NSF) awarded a $158,000 project grant under the Engineering program (CFDA 47.041) to the University of Connecticut (UConn) to explore the use of micromobility resources, such as dockless electric scooter sharing, to improve transportation access and equity in distressed neighborhoods in Hartford, Connecticut. The project aims to gather input from low-income riders, service providers, policymakers, and other stakeholders to co-design an equity-focused...
The National Science Foundation (NSF) awarded a $190,402 Project Grant under the Engineering program (CFDA 47.041) to The Trustees of the Stevens Institute of Technology in Hoboken, New Jersey. This collaborative research project develops a novel adaptive data collection framework to enable reliable data collection under severe time and resource constraints for automated post-disaster rapid damage assessment. The system leverages a Bayesian probabilistic modeling approach to dynamically...
This Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $100,000 to support the planning and development of a new shared-use, community-scale testbed for research on climate adaptation and disaster resilience. The testbed will serve as a cyber-physical environment representative of real-world community systems, enabling policy design, model integration, and theory development related to climate change impacts and natural hazards. The planning...
This National Science Foundation (NSF) Engineering CFDA 47.041 Project Grant award to Oklahoma State University is for $367,706 to investigate the use of place-based, human-centered networks leveraging micromobility, community social ties, and resilience hubs to enhance community resilience and equity in disaster response and recovery efforts. The research project, which includes a subaward to the University of North Carolina at Charlotte, aims to: 1) understand the functions, usage, and applicability of micromobility in different disaster scenarios; 2) examine how micromobility systems and local social networks can be integrated to better support communities in both everyday and disaster situations; and 3) validate the applicability of micromobility resilience hubs as connection points for micromobility and social networks in various disaster contexts. The study will utilize mixed-method analyses, including social network analysis, thematic analysis, discrete choice modeling, and agent-based simulations, to develop a cohesive network framework that will be validated through field testing in four case study communities across the United States.