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
1. 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. (under review)
2. Zhang, Z., Yin, D., Virrantaus, K., Ye, X., Wang, S., 2020. Modeling Population Dynamics: An Object-Oriented Space-Time Composite Model based on Social Media and Urban Infrastructure Data. Computational Urban Science (under review)
3. 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
4. ‡ 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.
5. 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.
6. 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.
7. Li, X., Dadashova, B., Yu, S., and Zhang, Z., 2020. Rethinking highway safety analysis by leveraging crowdsourced Waze data. Sustainability. (Accepted)
8. ‡ 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).
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.