Biography
Ziqi Li, Ph.D., earned his doctorate in Geography from Arizona State University in 2020.
I am a spatial data scientist interested in advancing statistical and machine learning models to explore and understand human behavior across different spatial contexts. I am one of the primary developers of Multi-scale Geographically Weighted Regression (MGWR) and a core developer of the Python Spatial Analysis Library (PySAL). My current work centers on developing spatially explicit machine learning models and improving their interpretability and explainability.
Education
Ph.D. Geography, Arizona State University, 2020
M.A. Geography, George Washington University, 2016
B.S. Geomatics, University of Waterloo, 2014
B. Eng. Remote Sensing, Wuhan University, 2014
Courses Taught
- Spatial Data Analysis
- Applied Spatial Statistics
- GIS Programming
Selected Publications
- Li, Z. (2024). GeoShapley: A game theory approach to measuring spatial effects in machine learning models. Annals of the American Association of Geographers.
- Li, Z. (2023). Leveraging explainable artificial intelligence and big trip data to understand factors influencing willingness to ridesharing. Travel Behaviour and Society.
- Fotheringham, A. S., Oshan, T. M., & Li, Z. (2023). Multiscale geographically weighted regression: Theory and practice. CRC Press.
- Fotheringham, A. S., & Li, Z. (2023). Measuring the unmeasurable: models of geographical context. Annals of the American Association of Geographers.
- Li, Z. (2022). Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Computers, Environment and Urban Systems.
