Cyberinfrastructure and Data-Intensive Research
The research group is part of the National Science Foundation $3 Million MIR Fostering Accelerated Sciences Transformation Education and Research Program
The research group has also developed a high-performance computer cluster based on advanced cyberinfrastructure and the latest docker technologies to host a massive amount of spatial data and computation power
With the increased frequency of natural hazards, disasters, and consequent massive losses, there is a critical need for building urban resilience and improving urban sustainability. Spatial decision support systems (SDSSs) have been well studied in the context of GIS and various related domains. New challenges and opportunities have arisen with regard to transforming SDSSs into intelligent capabilities for complex geospatial problem-solving and decision-making. The research group investigates intersections between GIScience, Decision Science, and Cyberinfrastructure to build scalable, high-performance, and open decision support software tools to support intelligent spatial decision-making in various application domains (e.g., disaster management, water resources management, agriculture risk management, national security, and critical infrastructure protection). Our research involved several inter-related sub-research themes:
1. Cyberinfrastructure and data-intensive research
2. Fast data streams and Internet of Things
2. Spatial uncertainty analysis and modeling
3. Social sensing and human knowledge acquisition
4. Human environment interactions
5. Spatiotemporal data modeling and population dynamics
We are actively recruiting M.S. and Ph.D. graduate students as well as visiting scholars who are interested in Geographic Information Science and Systems, Geo-computation, spatial decision-making, and data-Intensive research. Financial support and/or research/teaching assistantships are available to qualified students. If you are interested, please contact Dr. Zhe Zhang via email firstname.lastname@example.org
1. Li, Y., Gao, H., George, A., Zhang, Z., 2021. Constructing Reservoir Area-Volume-Elevation Curve. Applied Earth Observations and Remote Sensing, 14, 2249-2257.
2.Jiang, H., Hu, H., Li, B., Zhang, Z., Wang, S., Lin, T., 2021. Understanding the Non-Stationary Relationships Between Corn Yield and Meteorology via a Spatiotemporally Varying Coefficient Model. Agricultural and Forest Meteorology, 301,108340.
3. Zhang, Z., Yin, D., Virrantaus, K., Ye, X., Wang, S., 2021. Modeling Population Dynamics: An Object-Oriented Space-Time Composite Model based on Social Media and Urban Infrastructure Data. Computational Urban Science (Accepted)
4. Zhang, Z., 2020. Thematic accuracy. The Geographic Information Science & Technology Body of Knowledge (2ndQuarter 2020 Edition) John P. Wilson(ed). DOI: 10.22224/gistbok/2020.2.3
5. ‡ Li, D., Chaudhary, H., and Zhang*, Z., 2020. Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining. International Journal of Environmental Research and Public Health, 17(14), 4988.
6. Xu, B., Li, Y., Han, F., Zheng, Y., Ding, W., Zhang, C., Wallington, K. and Zhang, Z., 2020. The transborder flux of phosphorus in the Lancang-Mekong River Basin: Magnitude, patterns, and impacts from the cascade hydropower dams in China. Journal of Hydrology, 590: 125201.
7. Zhang, Z., Laakso, T., Wang, Z., Pulkkinen, S., Ahopelto, S., Virrantaus, K., Li, Y., Cai, X., Zhang, C., Vahala, R. and Sheng, Z., 2020. Comparative Study of AI-Based Methods—Application of Analyzing Inflow and Infiltration in Sanitary Sewer Subcatchments. Sustainability, 12(15): 6254.
8. Li, X., Dadashova, B., Yu, S., and Zhang, Z., 2020. Rethinking highway safety analysis by leveraging crowdsourced Waze data. Sustainability, 12.23 (2020): 10127.
9. ‡ Zhao* J., Zhang*, Z., Sullivan, C., 2019. Identifying anomalous nuclear radioactive sources using Poisson Kriging and mobile sensor networks, PLoS ONE, 14(5): e021613 (* Contributed equally to this work).
10. Armstrong, M., Wang., S., and Zhang., Z., 2019. The Internet of Things and fast data streams: prospects for geospatial data science in emerging information ecosystems. Cartography and Geographic Information Science, 46(1): 39-56.
11. Zhang, Z., 2019. Geospatial Software Institute: A Knowledge Hub for Driving Sustainable Geospatial Software Ecosystem. The 3rd NSF Workshop on Conceptualizing a National Geospatial Software Institute.
12. Zhang, Z., Hu, H., Yin, D., Kashem, S., Li, R., Cai, H., Perkins, D., and Wang, S., 2018. A CyberGIS-enabled multi-criteria spatial decision support system: a case study on flood emergency management. International Journal of Digital Earth, 12(11): 1364-1381.
13. Seppänen, H., Luokkala, P., Zhang, Z., 2018. Torkki, P., and Virrantaus, K. Critical infrastructure vulnerability- a method for identifying the infrastructure service failure interdependencies, International Journal of Critical Infrastructure Protection, 22: 25-38.
14. Zhang, Z., Demsǎ, U., Wang, S., and Virrantaus, K., 2017. A spatial fuzzy influence diagram for modelling spatial objects’ dependencies: a case study on tree-related electric outages. International Journal of Geographical Information Science, 32(2): 349-366.
15. Zhang, Z., and Virrantaus, K., 2016. Use of fuzzy decision-making approach in analysis of the vulnerability of street network for disaster management. Nordic Journal of Surveying and Real Estate Research, 11(2): 7-19.
16. Zhang, Z., Demsǎ, U., Rantala, J., and Virrantaus, K., 2014. A fuzzy multiple- attribute decision-making modelling for vulnerability analysis on the basis of population information for disaster management. International Journal of Geographical Information Science, 28(9): 1922-1939.
We got two articles accepted:
Jiang, H., Hu, H., Li, B., Zhang, Z., Wang, S., Lin, T., 2020. Understanding the Non-Stationary Relationships Between Corn Yield and Meteorology via a Spatiotemporally Varying Coefficient Model. Agricultural and Forest Meteorology.
Li, Y., Gao, H., George, A., Zhang, Z., 2020. Constructing Reservoir Area-Volume-Elevation Curve. Applied Earth Observations and Remote Sensing.
December 18, 2020
Texas A&M President’s Excellence Fund: T3 Grant
Human-Centered Decision Support System for Improving Urban Resilience in Disaster Management: $30,000
August 5, 2020
The research group developed a Health Space & Time Web App.
The app illustrates the number of COVID-19 confirmed cases at a spatiotemporal scale for the United States. It also demonstrates the spatiotemporal depressive symptoms caused by COVID-19 using social media data mining. The Health Space & Time application is available from here:
Feb 28, 2020
Convergent GIScience- Guest Lecture
Dr. Michael F. Goodchild
University of California, Santa Barbara
Jan 15, 2021
US Department of Transportation
Impact of COVID-19 Induced Active Transportation Demand on the Built Environment and Public Health: $90,000
September 1, 2020
NSF MRI Award #2019129
MRI: Acquisition of FASTER - Fostering Accelerated Sciences Transformation Education and Research
Funding amount: $3,090,000
Honggao Liu (Principal Investigator)
Raymundo Arroyave (Co-Principal Investigator)
Dilma Da Silva (Co-Principal Investigator)
Zhangyang Wang (Co-Principal Investigator)
Zhe Zhang (Co-Principal Investigator)
July 10, 2020
The research group has published a research paper on COVID-19 and social media data mining.
Li, D., Chaudhary, H., and Zhang, Z., 2020. Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining. International Journal of Environmental Research and Public Health, 17(14), 4988.
April 7, 2020
Symposium on Frontiers in CyberGIS and Geospatial Data Science: University Consortium For Geographic Information Science (UCGIS) - CyberGIS and Decision Support Systems. Click here to learn more.