Research

Shaowen Wang is trained as a geographic information scientist, geographer, and computer scientist. His research focuses on three interconnected themes: 1) theories and methods for cyberGIS and geospatial data science; integrated spatial artificial intelligence (AI) and cyberinfrastructure; and scalable solutions to complex geospatial problems and sustainability challenges. He has published extensively on these topics and has led multiple multi-institutional projects aimed at advancing cyberGIS, geospatial data science, and related scientific problem-solving. Several cyberGIS and geospatial computing capabilities created by his research program have been used by numerous researchers in a variety of domains (e.g., agriculture, bioenergy, computer science, environmental engineering and sciences, geography and spatial sciences, geosciences, humanities, public health, and social sciences) for tackling computational and data challenges to achieve broad and deep research advances. Currently, he is the founding director of the CyberGIS Center for Advanced Digital and Spatial Studies (CyberGIS Center) at the University of Illinois Urbana-Champaign and serves as the principal investigator and director of the national Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) funded by the National Science Foundation (NSF).

Summary of Extramural Research Funding (received from the U.S. NSF, Centers for Disease Control and Prevention (CDC), Department of Energy (DOE), Illinois Environmental Protection Agency (Illinois EPA), National Aeronautics and Space Administration (NASA), National Institutes of Health (NIH), National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Agriculture (USDA), U.S. Geological Survey (USGS), and industry): PI and co-PI for more than $60 million competitive research grants; senior personnel for contributing to sponsored research with tens of millions of U.S. dollars; and PI for tens of millions of normalized computing hours of NSF supercomputing resources.

Selected Publications (A Comprehensive List @ Google Scholar)

  • 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
  • Lyu, F., Wang, S. H., Han, S. Y., Catlett, C. E., and Wang, S. (2022) An integrated cyberGIS and machine learning framework for fine-scale prediction of urban heat island using satellite remote sensing and urban sensor network data. Urban Informatics, https://doi.org/10.1007/s44212-022-00002-4
  • Xu, Z., Wang, S., Stanislawski, L. V., Jiang, Z., Jaroenchai, N., Sainju, A. M., Shavers, E., Usery, E. L., Chen, L., Li, Z., and Su, B. (2021) An attention u-net model for detection of fine-scale hydrologic streamlines. Environmental Modelling & Software, https://doi.org/10.1016/ j.envsoft.2021.104992
  • Kang, J.-Y., Michels, A. C., Lyu, F., Wang, S. H., Agbodo, N., Freeman, V. L., and Wang, S. (2020) Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA. International Journal of Health Geographics, 19, https://doi.org/10.1186/s12942-020-00229-x
  • Wang, S. and Goodchild, M.F. (2019) CyberGIS for Geospatial Innovation and Discovery. Springer, Dordrecht, Netherlands, DOI: 10.1007/978-94-024-1531-5
  • Xu, Z., Guan, K., Casler, N., Peng, B., and Wang, S. (2018) A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 144: 423-434
  • Yin, D., Liu, Y., Hu, H., Terstriep, J., Hong, X., Padmanabhan, A., and Wang, S. (2018) CyberGIS‐Jupyter for reproducible and scalable geospatial analytics. Concurrency and Computation: Practice and Experience, https://doi.org/10.1002/cpe.5040
  • Wang, S. (2016) CyberGIS and spatial data science. GeoJournal, 81(6): 965-968
  • Wang, S., Liu, Y., and Padmanabhan, A. (2016) Open cyberGIS software for geospatial research and education in the big data era. SoftwareX, 5: 1-5
  • Cao, G., Wang, S., Hwang, M., Padmanabhan, A., Zhang, Z., and Soltani, K. (2015) A scalable framework for spatiotemporal analysis of location-based social media data. Computers, Environment and Urban Systems, 51: 70-82
  • Wang, S., Anselin, L., Bhaduri, B., Crosby, C., Goodchild, M.F., Liu, Y., and Nyerges, T.L. (2013) CyberGIS software: a synthetic review and integration roadmap. International Journal of Geographical Information Science (IJGIS), 27(11): 2122-2145
  • Wright, D. J. and Wang, S. (2011) The emergence of spatial cyberinfrastructure. Proceedings of the National Academy of Sciences, 108(14): 5488–5491
  • Wang, S. (2010) A cyberGIS framework for the synthesis of cyberinfrastructure, GIS, and spatial analysis. Annals of the Association of American Geographers, 100(3): 535-557
  • Wang, S. and Armstrong, M.P. (2009) A theoretical approach to the use of cyberinfrastructure in geographical analysis. IJGIS, 23(2): 169-193
  • Wang, S., and Liu, Y. (2009) TeraGrid GIScience Gateway: bridging cyberinfrastructure and GIScience. IJGIS, 23(5): 631-656
  • Wang, S., and Zhu, X-G. (2008) Coupling cyberinfrastructure and geographic information systems to empower ecological and environmental research. BioScience, 58(2): 94-95