Annals of the American Association of Geographers114(7), 1365-1385.
GeoShapley: A game theory approach to measuring spatial effects in machine learning models.
Li, Z. (2024)
Members
- Department
- Department of Geography
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.
Selected Publications
5- DOI
- DOITravel Behaviour and Society31, 284-294.
Leveraging explainable artificial intelligence and big trip data to understand factors influencing willingness to ridesharing.
Li, Z. (2023)
- DOICRC Press.194
Multiscale geographically weighted regression: Theory and practice.
Fotheringham, A. S., Oshan, T. M., & Li, Z. (2023).
- DOIAnnals of the American Association of Geographers113(10), 2269-2286
Measuring the unmeasurable: Models of geographical context
Fotheringham, A. S., & Li, Z. (2023)
- DOIComputers, Environment and Urban Systems96, 101845.
Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost.
Li, Z. (2022)