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Fangzheng Lyu

Assistant Professor
Portrait style shot Fangzheng Lyu
213 Wallace Hall (0115)
295 West Campus Drive
Blacksburg, VA
24061

My primary research interest lies in advancing GIScience, Geospatial Data Science and Computational Science to tackle complex geospatial problems and understand multi-scale urban dynamics using heterogeneous geospatial big data. Specifically, my research work focuses on 1) geospatial data science for understanding multi-scale urban dynamics, where I integrate machine learning with cyberGIS to predict and analyze complex urban phenomena (e.g. Urban Heat Islands) and develop frameworks for extracting information from heterogenous spatiotemporal data (e.g. high-frequency sensor data, social media data); 2) scalable spatial algorithms for solving complex geospatial problems, including developing scalable algorithms and models for geospatial analysis (e.g. remote sensing image fusion) using high-performance computing; and 3) democratization of data-intensive geographic research, where I innovate geospatial middleware approaches to simplify access to advanced cyberinfrastructure and enable collaborative geographic research and education.

Areas of Expertise:

  • GIS
  • Computational and Data Science
  • Urban Informatics
  • CyberGIS & Geospatial Computing
  • Geospatial AI

Education:

  • Ph.D. - University of Illinois Urbana-Champaign, 2024
  • M.S. - University of Illinois Urbana-Champaign, 2021
  • B.E. - University of Hong Kong, 2018
  • Principles of GIS (GEOG 2084)
  • Geospatial Data Science (GEOG 4984)
  • Elements of GIS (GEOG 5064 )
  • Spatial Data Science (GEOG 5984)
 
  • Ma, X., Song, Y., Lyu, F., Yang, Y., Wang, Y., Li, X., & Zhong, S. (2025). Revitalizing Cities: Growth and Change, 56(1), e70018. 
    https://doi.org/10.1111/grow.70018
  • Lyu, F., Yang, Z., Diao, C., & Wang, S. (2024). Multi-stream STGAN: A Spatiotemporal Image Fusion Model with Improved Temporal Transferability. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
    https://doi.org/10.1109/JSTARS.2024.3506879
  • Kang, Y., Lyu, F., & Wang, S. (2024). NetPointLib: Library for Large-Scale Spatial Network Point Data Fusion and Analysis. In Practice and Experience in Advanced Research Computing 2024: Human Powered Computing (pp. 1-4). 
    https://doi.org/10.1145/3626203.3670615 
  • Song, Z., Zhang, Z., Lyu, F., Bishop, M., Liu, J., & 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. Sustainability16(12), 5036. 
    https://doi.org/10.3390/su16125036
  • Lyu, F., Zhou, L., Park, J., Baig, F., Wang, S. (2024). Mapping dynamic human sentiments of heat exposure with location‑ based social media data. International Journal of Geographical Information Science, 1–24. 
    https://doi.org/10.1080/13658816.2024.2343063
  • Lyu, F., Wang, S., Han, S., 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 1, 6. 
    https://doi.org/10.1007/s44212-022-00002-4
  • Lyu, F., Kang, JY., Wang, S., Han, S.Y., Li, Z., Wang, S. (2021). Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19. In: Shaw, SL., Sui, D. (eds) Mapping COVID-19 in Space and Time. Human Dynamics in Smart Cities. Springer, Cham.
    https://doi.org/10.1007/978-3-030-72808-3_11
  • Wang, S., Lyu, F., Wang, S., Catlet, C., Padmanabhan, A., Soltani, K. (2021). Integrating CyberGIS and Urban Sensing for Reproducible Streaming Analytics. Urban Informatics, ISBN 978‑ 981‑15‑8983‑6. Springer Sinapore.
    https://doi.org/10.1007/978-981-15-8983-6_36
  • Lyu, F., Xu, Z., Ma, X., Wang, S., Li, Z., Wang, S. (2021). A vector-based method for drainage network analysis based on LiDAR data. Computers & Geosciences, 156, 104892. 
    https://doi.org/10.1016/j.cageo.2021.104892
  • Kang, JY., Michels, A., Lyu, F., Wang, S., Agbodo, N., Freeman, V., Wang, S. (2020). Rapidly measuring spatial accessibility of COVID‑19 healthcare resources: a case study of Illinois, USA. International Journal of Health Geographics 19, 36. 
    https://doi.org/10.1186/s12942-020-00229-x
  • Lyu, F., et al. (2019). Reproducible hydrological modeling with CyberGIS-Jupyter: a case study on SUMMA. In Proceedings of the practice and experience in advanced research computing on rise of the machines (learning) (pp. 1-6). 
    https://doi.org/10.1145/3332186.3333052