My research focuses on the methodological development of spatially explicit statistical and interpretable machine learning models. I am one of the primary developers of multi-scale geographically weighted regression (MGWR). I am also broadly interested in the application of advanced spatial analysis and modeling in the fields of public health, urban analytics, political geography, and remote sensing.
1. 2021 – present, Lecturer, University of Glasgow
2. 2020-2021, Visiting Assistant Professor, University of Illinois, USA
3. 2016 – 2020, PhD researcher, Arizona State University, USA
Active research projects:
1. Investigating the utility of local interpretable machine learning and explainable AI for spatial data analysis and modelling, Alan Turning Institute (2022)
2. Advancing Methods for Spatial Analysis in Local Modeling, US National Science Foundation (2021- 2024)
1. 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.
2. Fotheringham, A.S., Yu, H., Wolf, L., Oshan T., Li,Z. (2022). On the notion of ‘bandwidth’ in geographically weighted regression models of spatially varying processes. International Journal of Geographical Information Science.
3. Sachdeva, M., Fotheringham, A. S., Li, Z. (2022). Quantifying the intrinsic value of housing neighborhoods using MGWR. Journal of Housing Research.
4. Li, Z. & Fotheringham, A. S. (2022). The Spatial and Temporal Dynamics of Voter Preference Determinants in Four U.S. Presidential Elections (2008-2020). Transactions in GIS.
5. Zhao, Q., Li, Z., Shah D., Fischer, H., Solís, P. & Wentz, E. (2021). Understanding the Interaction between Human Activities and Physical Health under Extreme Heat Environment in Phoenix, Arizona. Health and Place.