NSF Convergence Accelerator Track E: Combining High-Resolution Climate Simulations with Ocean Biogeochemistry, Fisheries and Decision-Making Models to Improve Sustainable Fisheries
Fish and shellfish populations are a vital source of protein for many of the world’s people, and several of the largest are found along the eastern boundaries of the Pacific and Atlantic Oceans, where cold, deep water moves towards the surface, bringing nutrients that support both production by plants (phytoplankton) and the fish populations that feed on them.
This project aims to use these advancements to improve forecasts of the fisheries potential of the California Current Ecosystem and improve decision making by managers and other stakeholders. The project will couple the output from such a high-resolution model simulation with the Marine Biogeochemistry Library and Fisheries Size and Functional Type models, thus incorporating physics, chemistry and biology with climate variability. The results will be integrated with a prototype, web-based decision support system, that uses mathematical decision analysis capabilities, to assist fisheries managers to model the complex, climate-related decision problems on which fisheries production depends. This is vital to ensure that the region can continue to support a sustainable fishery in the long term and the communities that depend on fishing for a living.
Texas Youth Geography Network: Spatial Learning Tools for Advancing Youth Geography Education- Funded by National Geographic Society
This project aims to form a Texas Youth Geography Education Network (TYGE) by following Diversity, Equity, and Inclusion principles through the partnership with K-12 schools and non-profit organizations to promote “Play-in-Learn” teaching modules for advancing Geography education.
Impact of COVID-19 Induced Active Transportation Demand on the Built Environment and Public Health (U.S. Department of Transportation, $90,000, 2021-2022)
The research team will work closely with different stakeholders in Texas El Paso region, including the regional transit agencies (Sun Metro and El Paso County Transit), COVID-19 and Bicycle and Pedestrian Groups of the City of El Paso, Camino Real Regional Mobility Authority (CRRMA), Texas Department of Transportation (TxDOT) El Paso District, and El Paso Metropolitan Planning Organization (MPO), to develop data-driven tools and recommendations for implementing bicycle- and pedestrian friendly infrastructure to meet and maintain the new challenges caused by COVID-19.
Team has hosted a stakeholders' workshop on July 21st, 2021.
A Hybrid Decision Support System for Driving Resiliency in Texas Coastal Communities (NOAA Sea Grant, $300,000, 2020-2022)
This research seeks to augment current flood management practices in Texas coastal communities using citizen science, artificial intelligence (AI), and decision science, and cyberinfrastructure. In this project, we use citizen science and machine learning to compare pre-flood and post-flood photos of the same traffic “STOP” sign location to estimate the depth of floodwater at street level. The traffic “STOP” signs are used as benchmarks since their shapes and sizes are standardized anywhere in the country. Generated data will be further incorporated in a CyberGIS-enabled spatial decision support tool for residents and first responders to improve the quality and timeliness of decision-making in the event of a flood.
Our high-performance computer cluster is hosting the BluPix application at:
K-12 Geography and GIS education program (Texas Department of State Health Services, $10,000, 2020-2022)
The research group has organized several lab sessions and lectures for the K-12 students that come from Texas A&M Chinese School, Texas A&M Consolidated High School, and Allen Academy. As a result, K-12 students have published a poster at AAG 2020 annual meeting on the topic of "The Pathological and Economic Consequences of Houston's Air Pollution Catastrophe: A Youth's Perspective".
2020 Saari, S., Wang, R., Cairns, J., Wang, C., Li, A., Yang, E., and Brody, A., Urban Social Vulnerability Assessment under COVID-19 and Natural Disasters. University Consortium for Geographic Information Science Symposium Poster. Texas A&M GIS and Geography K-12 Education Program.
Available from the web: https://www.ucgis.org/symposium-2020-poster-gallery
Saari S., Wang, C., Cairns J., Li, A., Yang, E., Cairns, L., 2020. The Pathological and Economic Consequences of Houston’s Air Pollution Catastrophe: A Youth's Perspective. AAG Annual Meeting poster. Available from the web:
Innovation [X] Project (Funded by Texas A&M School of Innovation, $20,000, 2020-2021)
Natural disasters globally cause significant human loss and economic damage. Disaster responders often need to make quick decisions in complex situations under heavy duress. The decision goals are usually achieved through inquiry into a series of spatial parameters closely tied to specific decision objectives and their associated evaluation criteria based on diverse social, socioeconomic, and demographic conditions. In this project, we aim to design an interactive and collaborative spatial decision support system (SDSS) based on advanced cyberinfrastructure, WebGIS, and citizen science to improve situational awareness in disaster management. The proposed SDSS considers spatial and social vulnerability priorities to enhance knowledge elicitation and sharing among a diverse range of disaster responders and communities.
We published a poster at the 46th Annual Natural Hazards Research and Applications Workshop:
Social Vulnerability, Mobility, and COVID-19 Spatial Mortality Patterns (NSF Funded Converge COVID-19 Working Groups, $1000, 2020)
This Working Group aims to observe the spatial variation of the COVID-19 mortality rate with various sociodemographic and spatial variables. This science-driven project integrates advanced cyberinfrastructure, geospatial statistical models, and novel AI algorithms to analyze spatial COVID-19 mortality patterns.
A Spatial Decision Support System in Response to the COVID-19 Pandemic
Texas A&M Institute of Data Science, $ 27,000, 2020-2021
The research team is working on analyzing massive social media data and mobility data to predict the disease spread pattern at a spatiotemporal scale.
We have developed a Health Space &Time web application to visualize people's risk perception under COVID-19 using social meida data mining and artificial intelligence.
The Health Space&Time web application is available at:
FASTER -Fostering Accelerated Scientific Transformations, Education, and Research (National Science Foundation, $3,000,000, 2020-2023)
The National Science Foundation has funded a $3 million high-performance data-analysis and computing instrument project, named FASTER (Fostering Accelerated Scientific Transformations, Education, and Research). FASTER will enable transformative advances in scientific fields that rely on artificial intelligence and machine learning (AI/ML) techniques, big data practices, and high-performance computing (HPC) technologies. The FASTER platform removes significant bottlenecks in research computing by leveraging a technology that can dynamically allocate resources to support workflows.