Publications

Books and chapters

1. Fotheringham, A.S., Oshan, T., & Li, Z. (2023). Multiscale Geographically Weighted Regression: Theory and Practice (1st ed.). CRC Press. doi: 10.1201/9781003435464

2. Li, Z. NoSQL Databases (2018). The Geographic Information Science & Technology Body of Knowledge (2nd Quarter 2018 Edition), John P. Wilson (Ed).  Link

Peer-reviewed articles

*Corresponding     †Student

1. Li, Z. (2024). GeoShapley: A Game-Theory Method for Measuring Spatial Effects in Machine Learning Models. Annals of the American Association of Geographers. Doi: 10.1080/24694452.2024.2350982.  arXiv preprint

2. Russell, A.†, Li, Z* & Wang, M. (2023). Equalizing Urban Agriculture Access in Glasgow: A Spatial Optimization Approach. International Journal of Applied Earth Observation and Geoinformation. doi: https://www.sciencedirect.com/science/article/pii/S1569843223003497

3. Yao, J., Li, Z.*, Zhang, X., Liu, C., & Ren, L. (2023). A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility. The 12 International Conference on Geographic Information Science (GIScience), Leeds, UK. doi: 10.4230/LIPIcs.GIScience.2023.86

4. Fotheringham, A. S. & Li, Z.* (2023). Measuring the Unmeasurable: Models of Geographical Context. Annals of the American Association of Geographers. Doi: 10.1080/24694452.2023.2227690.

5. Lei, F., Ma, Y., Fotheringham, A. S., Mack, E., Li, Z. Sachdeva, M., Bardin, S. & Maciejewski, R. (2003). GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation. IEEE Transactions on Visualization and Computer Graphics (TVCG). Link

6. Zhang, S† & Li, Z. (2003). Geographically Weighted Cronbach’s Alpha. Geographical Information Science Research UK 2023, Glasgow, UK. Doi: 10.5281/zenodo.7834162

7. Li, Z.* (2023). Leveraging explainable artificial intelligence and big trip data to understand factors influencing willingness to ridesharing. Travel Behaviour and Society, 31, 284-294.

8. 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.  Link (ESI 0.1% Cited)

9. Xi, G., Lu, Y., Beene, D., Li, Z., Hu, T.,  Morgan, M., & Yan Lin. (2022). Understanding public perspectives on fracking in the United States using social media big data. Annals of GIS. Link

10. 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. Link

11. Sachdeva, M., Fotheringham, A. S., Li, Z. (2022). Quantifying the intrinsic value of housing neighborhoods using MGWR. Journal of Housing Research. Link.

12. 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. Link.

13. Li, Z.* & Xu, T†. Understanding Factors Influencing Willingness to Ridesharing Using Big Trip Data and Interpretable Machine Learning. Geographical Information Science Research UK 2022, Liverpool. doi: 10.5281/zenodo.6411504

14. 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. Link

15. Sachdeva, M., Fotheringham, A. S., Li, Z. & Yu, H. (2021). Are we modelling spatially varying processes or non-linear relationships? Geographical Analysis. Link

16. Rey, S., Anselin, L., Amaral, P., Arribas-Bel, D., Cortes, R., Gaboardi, J., Kang, W., Knaap, E., Li, Z., LumnitzU, S., Oshan, T., Shao, H., & Wolf, L. (2021). The PySAL ecosystem: philosophy and implementation. Geographical Analysis. Link

17. Kurji, J., Thickstun, C., Bulcha, G., Taljaard, M., Li. Z. & Kulkarni, M. (2021). Spatial variability in factors influencing maternal health service use in Jimma Zone, Ethiopia: a geographically-weighted regression analysis. BMC Health Services Research. 21(454).  Link

18. Fotheringham, A. S., Li, Z., & Wolf, L. J.  (2021). Scale, Context and Heterogeneity: A Spatial Analytical Perspective on the 2016 US Presidential Election. Annals of the American Association of Geographers. Link

19. Wang, C., Li, Z., Matthews, M., Praharaj, S., Karna, B., Solis, P.  (2020) The Spatial Association of Social Vulnerability with COVID-19 Prevalence in the Contiguous United States. International Journal of Environmental Health Research. Link.

20. Li, Z.*,  Fotheringham, A. S. (2020). Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science. 34(7), 1378-1397. Link (AAG Nystrom Award Winning Paper)

21. Li, Z.*,  Fotheringham, A. S., Oshan, T. & Wolf, L. J. (2020). Measuring bandwidth uncertainty in multiscale geographically weighted regression using Akaike weights. Annals of the American Association of Geographers. Link (AAG-SAM John Odland Award Winning Paper)

22. Yu, H., Fotheringham, A. S., Li, Z., Oshan, T., & Wolf, L. J. (2020). On the measurement of bias in geographically weighted regression models. Spatial Statistics.  Link.

23. Fotheringham, A. S., Han, Y., & Li, Z. (2019). Examining the influences of ambient air quality in China’s cities using multi-scale geographically weighted regression. Transactions in GIS , 23(6), 1444-1464.  Link

24. Li, Z.*, Fotheringham, A. S., Li, W., & Oshan, T. (2019). Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations. International Journal of Geographical Information Science, 33(1), 155-175.  Link

25. Oshan, T., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). mgwr: A Python implementation of multi-scale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information. 8(6), 286.  Link (ESI 1% Cited)

26. Oshan, T., Wolf, L. J., Fotheringham, A. S., Kang, W., Li, Z., & Yu, H. (2019). A comment on geographically weighted regression with parameter-specific distance metrics, International Journal of Geographical Information Science, 33(7), 1289-1299.  Link 

27. Yu, H., Fotheringham, A. S., Li, Z., Oshan, T., Kang, W., & Wolf, L. J. (2019). Inference in multiscale geographically weighted regression. Geographical Analysis. 52(1), 87-106.  Link (ESI 1% Cited)

28. Li, Z.*, Zhang, Z., & Davey, K. (2015). Estimating geographical PV potential using LiDAR data for buildings in downtown San Francisco. Transactions in GIS, 19(6), 930-963.  Link