Recent Projects
1.NSF CAREER (#2339174): A Cyberinfrastructure Enabled Hybrid Spatial Decision Support System for Improving Coastal Resilience to Flood Risks (Dr. Zhang as Sole PI, $568,302)
This CAREER project builds a Hybrid Spatial Decision Support System that integrates scalable geospatial data and visualization tools into a cyberinfrastructure-enabled framework to support decision-making in flood management. This project also establishes a scientific roadmap to advance disaster decision science using advanced cyberinfrastructure, geospatial artificial intelligence, and education activities to educate communities to better prepare for flood hazards. In the Hybrid Spatial Decision Support System, a high-performance cyberinfrastructure-based interface accelerates reading and visualizing disaster-related Network Common Data Form (NetCDF) data. Another innovation is to combine the data-driven approach with expert-driven decision analysis to enable a more accurate, comprehensive, and transparent flood risk assessment that bridges the gap between the digital world and human perception of risk. Community engagement activities and use-inspired research are used to evaluate the level of trust and transparency of using geospatial artificial intelligence and decision-making models in flood risk prediction. Finally, the project integrates research outcomes into educational curricula and activities to engage students and researchers in high-performance computational thinking for disaster management research.
2. Collaborative Research(#2519476): CyberTraining Implementation Medium: Broadening Adoption of Cyberinfrastructure and Geospatial Artificial Intelligence for Disaster Management (Dr. Zhang as PI, $1million, 2025-2029)
The primary goal of this project is to establish a CyberTraining Network for Geospatial Artificial Intelligence (GeoAI) in Disaster Management. Through the proposed CyberTraining activities, the project will enable disaster management students, researchers, and professionals, to strengthen their capabilities in cyberinfrastructure and GeoAI. By broadening access to GeoAI and cyberinfrastructure, the project will support research workforce development across various disciplines, including disaster science, geosciences, transportation, and engineering. The team will collaborate with academia, industry, non-profit organizations, and government agencies to provide open access to disaster-related data, training materials, and cyberinfrastructure resources. To ensure scalability and sustainability, the project will be supported by core partner organizations and integrated with National Science Foundation funded cyberinfrastructure, disaster data providers, professional societies, and industry stakeholders. Students and educators will be actively engaged through a participant award program, expanding educational and career development opportunities. Furthermore, this project will enable disaster researchers to advance their use of cyberinfrastructure and GeoAI in disaster management, enhance computational skills, and improve data-driven decision-making for increased disaster resilience. The project is designed to significantly improve the well-being of populations affected by natural hazards and disasters.
For more information on projects, please visit project page: https://dev.ctdm.org/
3. Collaborative Research: Conference: NAIRR Pilot Expansion: FA1:Utilizing NAIRR Pilot Resources for Building Sustainable Blue Economy (Dr. Zhang as lead PI, $299,997)
The Gulf of America faces significant challenges for Blue Economy research, including environmental degradation from pollution and overfishing, as well as the impacts of natural disasters. These issues strain marine ecosystems and coastal communities, creating barriers to sustainability. Similarly, the mid-Atlantic region, such as Maryland, suffers from agriculture, urban runoff, and industrial activities that degrade water quality and significantly impact fish populations and the overall health of the fishery. Hawaii, meanwhile, encounters unique challenges due to its geographic isolation and heavy reliance on ocean resources. This project aims to address these challenges by establishing coordinated efforts to improve Blue Economy research in these regions, enhancing funding for innovative research, and strengthening partnerships across sectors to build a Blue Economy research network. This project will bring together students, faculty, and researchers from U.S. institutions, along with participants from broad fields, including GIScience, oceanography, computer science, biology, and civil engineering. The goal is to promote AI education and workforce development in Blue Economy research.
The workshop will offer training opportunities to help research communities effectively navigate NAIRR pilot resources, including learning how to access and use these resources for ocean and coastal sustainability analysis. All workshop and training materials will be made publicly available through a project GitHub repository and website, ensuring broad accessibility and ongoing learning opportunities.
4. NSF CyberTraining (#2321069): Implementation: Small: Broadening Adoption of Cyberinfrastructure and Research Workforce Development for Disaster Management (Dr. Zhang as PI, $454,847)
Disasters are prominent global issues which simultaneously pose threats to multiple countries or regions. Disaster management is gradually empowered by increasing geospatial big data awareness and growing computing capabilities to produce spatial vulnerability and situational understanding for supporting timely decisions. This project will establish an international CyberTraining for Disaster Management (CTDM) network in which disaster research communities can broaden their cyberinfrastructure (CI) and geospatial skills by participating in the proposed training activities. The project will establish a CI-enabled geospatial disaster science network among academic institutions, governmental agencies, hazards research centers, industry, and educational organizations to leverage the expertise of pertinent communities in developing training materials for preparing the next-generation workforce. A novel training curriculum is developed to consist of various training modalities such as summer schools, workshop sessions, and online webinars, which utilize CI and scalable geospatial analytics for effective disaster management practice.
Please visit our project website to learn more about out CyberTraining activities.
Publications:
1. Song, Z., Zhang, Z., Lyu, F., Bishop, M., Liu, J., and Chi, Z., 2024. From Individual Motivation to Geospatial Epidemiology: A Novel Approach Using Fuzzy Cognitive Maps and Agent-Based Modeling for Large-Scale Disease Spread. Sustainability, 2024, 16 (12):5036.
2. Michels, A. C., Padmanabhan, A., Xiao, Z., Kotak, M., Baig, F., and Wang, S., 2024. CyberGIS-Compute: Middleware for Democratizing Scalable Geocomputation. SoftwareX, https://doi.org/10.1016/j.softx.2024.101691
3. Kang, Y., Lyu, F., and Wang, S.,2024. NetPointLib: Library for Large-Scale Spatial Network Point Data Fusion and Analysis. In: Proceedings of Practice and Experience in Advanced Research Computing (PEARC’24), Providence, Rhode Island, USA, July 21 – 25, 2024
4. Michels, A. C., Kotak, M., Padmanabhan, A., Speaks, J., and Wang, S.,2024. Providing Accessible Software Environments
5. Song, Z., Zhang, Z., Lyu, F., Bishop, M., Liu, J., and Chi, Z., 2024. From Individual Motivation to Geospatial Epidemiology: A Novel Approach Using Fuzzy Cognitive Maps and Agent-Based Modeling for Large-Scale Disease Spread. Sustainability, 2024, 16 (12):5036.
6. Michels, A. C., Padmanabhan, A., Xiao, Z., Kotak, M., Baig, F., and Wang, S., 2024. CyberGIS-Compute: Middleware for Democratizing Scalable Geocomputation. SoftwareX, https://doi.org/10.1016/j.softx.2024.101691
7. Kang, Y., Lyu, F., and Wang, S.,2024. NetPointLib: Library for Large-Scale Spatial Network Point Data Fusion and Analysis. In: Proceedings of Practice and Experience in Advanced Research Computing (PEARC’24), Providence, Rhode Island, USA, July 21 – 25, 2024
8. Michels, A. C., Kotak, M., Padmanabhan, A., Speaks, J., and Wang, S.,2024. Providing Accessible Software Environments Across Science Gateways and HPC. In: Proceedings of Practice and Experience in Advanced Research Computing (PEARC’24), Providence, Rhode Island, USA, July 21 – 25, 2024
CyberTraining Workshops:
2025 Texas A&M CyberTraining Summer School
5. NSF Convergence Accelerator Track E (#2137684): 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.
Publications:
1. Xu, G., Chang, P., Ramachandran, S., Danabasoglu, G., Yeager, S., Small, J., Zhang, Q., Jing, Z. and Wu, L., 2022. Impacts of model horizontal resolution on mean sea surface temperature biases in the community earth system model. Journal of Geophysical Research: Oceans, 127(12), p.e2022JC019065.
2. Chang, P., Xu, G., Kurian, J., Small, R.J., Danabasoglu, G., Yeager, S., Castruccio, F., Zhang, Q., Rosenbloom, N. and Chapman, P., 2023. Uncertain future of sustainable fisheries environment in eastern boundary upwelling zones under climate change. Communications Earth & Environment, 4(1), p.19.
3. Song, Z., Chapman, P., Tao, J., Chang, P., Gao, H., Liu, H., ... & Zhang, Z. (2024). Mapping the Unheard: Analyzing Tradeoffs Between Fisheries and Offshore Wind Farms Using Multicriteria Decision Analysis. Annals of the American Association of Geographers, 114(3), 536-554.
4. Zhang Z., Song, Z., Chang, P., Tommasi, D., Petrik, C., Morrison, M., Danabasoglu, G., Tao, J., Chapman, P., Stephens, K., (2026). Sustainable Blue: A Participatory Spatial Decision Support System for Fishery Management. Annals of the American Association of Geographers Special Issue New Messages/New Media (submitted)
Sustainable Blue Application User Interface:
Sustainable Blue integrate climate and biochemistry model to predict the fish migration pattern at spatial and temporal scale.
2025 University Consortium for Geographic Information Science Symposium CyberTraining Workshop, Laramie, Wyoming
2023 The 49th Annual Natural Hazards Center Workshop, Broomfield, Colorado
2023 American Association of Geographers Annual Meeting CyberTraining Workshop,
Honolulu, Hawaii
6. 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 project 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, especiallyvulnerable populations.In July 2023, we have organized a community workshop.
7. 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. The team has completed nine story maps for the following topics:
AP Human Geography:
Unit 2: Population and Migration Patterns and Processes
Unit 3: Cultural Patterns and Processes
Unit 4: Political Patterns and Processes
Unit 5: Agriculture and Rural Land-Use Patterns and Processes
Unit 6: Cities and Urban Land-Use Patterns and Processes
Unit 7: Industrial and Economic Development Patterns and Processes
AP Environmental Science：
8. 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.
Publications:
Hu, N., Zhang, Z., Duffield, N., Li, X., Dadashova, B., Wu, D., ... & Zhang, Z. (2024). Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data. Computational Urban Science, 4(1), 6.
Li, X., Yu, S., Huang, X., Dadashova, B., Cui, W., & Zhang, Z. (2022). Do underserved and socially vulnerable communities observe more crashes? A spatial examination of social vulnerability and crash risks in Texas. Accident Analysis & Prevention, 173, 106721.
9. 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. The team has developed BluPix application.
Publications:
Hillin, J., Alizadeh, B., Li, D., Thompson, C. M., Meyer, M. A., Zhang, Z., & Behzadan, A. H. (2024). Designing user-centered decision support systems for climate disasters: what information do communities and rescue responders need during floods?. Journal of Emergency Management (Weston, Mass.), 22(7), 71-85.
Alizadeh, B., Li, D., Hillin, J., Meyer, M. A., Thompson, C. M., Zhang, Z., & Behzadan, A. H. (2022). Human-centered flood mapping and intelligent routing through augmenting flood gauge data with crowdsourced street photos. Advanced Engineering Informatics, 54, 101730.
Li, D., Zhang, Z., Alizadeh, B., Zhang, Z., Duffield, N., Meyer, M. A., ... & Behzadan, A. H. (2024). A reinforcement learning-based routing algorithm for large street networks. International Journal of Geographical Information Science, 38(2), 183-215.
