Fangzheng Lyu
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