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Fangzheng Lyu

Assistant Professor
Portrait style shot Fangzheng Lyu
213 Wallace Hall

B.E. in Computer Engineering, University of Hong Kong, 2018

M.S. in Geography, University of Illinois Urbana-Champaign, 2021

Ph.D. in Geography, University of Illinois Urbana-Champaign, 2024

Areas of interest:

  • GIS

  • Computational and Data Science

  • Urban Informatics

  • CyberGIS & Geospatial Computing

  • Geospatial AI

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.

Teaching duties

GEOG2084 Principles of GIS

GEOG 5064 Elements of GIS

Recent publications:

  • 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