Biography
I am a spatial data scientist interested in advancing statistical and machine learning models to understand human behavior across different spatial contexts. I am the developer of GeoShapley, one of the primary developers of Multi-scale Geographically Weighted Regression (MGWR) and a core developer of Python Spatial Analysis Library (PySAL). My current work centers on developing spatially explicit machine learning models and improving their interpretability and explainability.
Department of Geography
Ph.D. in Geography
Arizona State University, 2020
- Spatial Statistics
- GeoAI
- Explainable AI (XAI)
Postdoctoral Enrichment Award, Alan Turing Institute, UK
2023
J. Warren Nystrom Award, American Association of Geographers
2021
John Odland Award, Spatial Analysis and Modelling Group, American Association of Geographers
2020
- Annals of the American Association of Geographers
114(7), 1365-1385.
GeoShapley: A game theory approach to measuring spatial effects in machine learning models.
Li, Z. (2024)
- Travel Behaviour and Society
31, 284-294.
Leveraging explainable artificial intelligence and big trip data to understand factors influencing willingness to ridesharing.
Li, Z. (2023)
- CRC Press.
194
Multiscale geographically weighted regression: Theory and practice.
Fotheringham, A. S., Oshan, T. M., & Li, Z. (2023).
- Annals of the American Association of Geographers
113(10), 2269-2286
Measuring the unmeasurable: Models of geographical context
Fotheringham, A. S., & Li, Z. (2023)
- Computers, Environment and Urban Systems
96, 101845.
Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost.
Li, Z. (2022)