This $191,980 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will fund research examining the impact of displacement on the development of social preferences and trust in children. The project, conducted by the University of Chicago, will study how displacement caused by conflicts and climate change affects children's and adolescents' social behaviors, trust, cooperation, and adherence to social norms. The research...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) aims to revolutionize the understanding and prediction of human mobility patterns. The $300,000 award to Lehigh University, to be executed from September 1, 2024 to August 31, 2027, will support the development of privacy-preserving, federated neural networks to analyze large-scale, cross-domain human mobility data from sources like smartphones, payment...
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 grant supports a 3-year research project that examines the impacts of natural disasters on community resilience and spatial disparities. The project has three key components: 1) analyzing disruptions to resident mobility patterns using mobile phone data, 2) investigating spatial variation in requests for government assistance, and 3)...
This Project Grant award from the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program provides $323,520 to the University of South Florida to conduct a longitudinal study on post-disaster population movement and social integration. The principal investigator will interview 54 individuals who relocated after hurricanes in 2017 to understand how demographic factors, place-making, and rebuilding processes shape long-term settlement decisions and community...
This Project Grant award of $458,784 from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (CFDA 47.084) program supports a research project at Lehigh University focused on enhancing disaster response and recovery capabilities. The project aims to develop advanced, physics-informed machine learning models that integrate autonomous systems and numerical hazard models to improve real-time damage assessment and decision-making for natural disasters such as...
This National Science Foundation project grant of $149,999 supports research at the University of Massachusetts Amherst to improve climate migration modeling and planning through August 2023. Funded under the Engineering program (CFDA 47.041), key products include refining scientific models to better project domestic population movement driven by environmental disasters. Researchers will enhance data sources such as the American Community Survey and novel online data to dynamically simulate...
This Project Grant award of $500,000.00 from the National Science Foundation's Geosciences Program (CFDA 47.050) is funding the development of a transformative AI-based framework for generating high-fidelity, physically consistent, and uncertainty-calibrated geoscience data. The goal is to overcome limitations in observational infrastructure and computational cost to produce enhanced datasets that can improve decision-making for disaster preparedness, emergency response, and infrastructure...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $255,000 Project Grant to the University of Florida (UF) under the Mathematical and Physical Sciences program (CFDA 47.049) to develop geospatial modeling and risk mitigation tools for hurricane evacuation and disaster response. The 3-year project leverages human mobility data from mobile devices and vehicle traffic monitoring to build statistical and optimization models that can improve the efficacy of disaster...
This $180,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences supports the development of a multimodal transformer-based model for time-series prediction and spatiotemporal analysis. The project aims to create algorithms for forecasting time-series, predicting spatial dynamics, and detecting anomalies, which can be applied to data analysis and high-consequence decision-making. The research will address how to utilize contextual information with...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a 3-year, $100,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to Rutgers, The State University located in New Brunswick, New Jersey. The grant supports the development of advanced statistical models and software to predict and assess the likelihood of extreme geopolitical events with quantified uncertainty. The project aims to construct a comprehensive, data-driven prediction...
This $249,588 federal Project Grant award, funded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), supports research by Georgetown University aimed at developing Bayesian spatiotemporal transfer learning models to forecast forced displacement. The project seeks to identify leading indicators of displacement, integrate knowledge from previous crises, and construct statistical models that can provide short-term predictions of displaced person flows during emerging crises. The investigators will develop a general Bayesian transfer learning framework applicable to non-Gaussian, spatiotemporally correlated data, and apply it to forecasting forced displacement in Ukraine, Bangladesh, and Sudan. This research is expected to advance migration prediction capabilities and enable rapid dissemination of humanitarian aid during displacement crises. The award period runs from Sep. 1, 2024 to Aug. 31, 2027.