NSF Convergence Accelerator Track E: Combining High-Resolution Climate Simulations with Ocean Biogeochemistry, Fisheries and Decision-Making Models to Improve Sustainable Fisheries (Dr. Zhang as PI, $750,000)
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.
NASA Earth Science Equity and Environmental Justice ROSES Project (Dr. Zhang as Institutional PI, $149, 163)
Climate change is causing extreme heat in American cities. Previous heat exposure assessments and predictions are from a top-down policy perspective, neglecting the viewpoints of different stakeholders, especially vulnerable populations. The City of Oklahoma City (OKC), OK has recently focused on urban heat mitigation by a series of sustainable plans and actions due to the increasing frequency and intensity of extreme heat events. There is an urgent need in OKC to gain a comprehensive picture of the urban areas and populations vulnerable to heat, as well as preferences and recommended decisions from different stakeholders, to conduct sustainable and equitable planning. The objective of this proposal is to develop an innovative Heat Exposure Index (HEI) based on NASA data and a human-environmental energy budget model; a Heat Vulnerability Index (HVI) by integrating multi-dimensions of heat vulnerable indicators, as well as a spatial decision support system to promote heat-related policymaking processes among different stakeholders, especially
In July 2023, we have organized first community workshop.
Texas Youth Geography Network: Spatial Learning Tools for Advancing Youth Geography Education- Funded by National Geographic Society (Dr. Zhang as PI, 42,978)
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 - funded by U.S. Department of Transportation(Dr. Zhang as Co-PI, $90,000)
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 funded by NOAA Sea Grant (Dr. Zhang as Co-PI, $299, 995)
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 funded by Texas Department of State Health Services (Dr. Zhang as PI, $10,000)
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
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:
A Spatial Decision Support System in Response to the COVID-19 Pandemic funded by Texas A&M Institute of Data Science( Dr. Zhang as PI, $ 27,000)
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 funded by National Science Foundation(Dr. Zhang as Co-PI, $3,000,000)
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.