Publications

Book

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

Peer-reviewed articles

*Corresponding     †Student

2025

1. Li, Z. Explainable AI in Spatial Analysis. In: Huang, X., Wang, S., Wilson, J., Kedron, P. (eds) GeoAI and Human Geography. Springer Geography. Springer, Cham. https://doi.org/10.1007/978-3-031-87421-5_5

2. Li, Z.* and Peng, Z. Can Moran Eigenvectors Improve Machine Learning of Spatial Data? Insights from Synthetic Data Validation. Geographical Analysis. 10.1111/gean.70011

3. Yuan, X.†, Li, Z., Basiri, A., & Wang, M. (2025). Where England’s Cities Are Growing: Evidence from Big Building Footprint Data and Explainable AI. Habitat International. doi.org/10.1016/j.habitatint.2025.103457

4. Zhang, S† and Li, Z. (2025). Geographically Weighted Cronbach’s Alpha (GWalpha): an Exploratory Local Measure of Reliability for Scale Construction. Geographical Analysis. https://doi.org/10.1111/gean.70021

5. Luo, P., Li, Y., Song, Y., Li, Z., & Meng, L. (202) Measuring univariate effects in the interaction of geographical patterns. International Journal of Geographical Information Science. doi: 10.1080/13658816.2025.2526042

6. Sutton, D.†, Basiri, A., and Li, Z. (2025). Exploring a Diagnostic Test for Missingness At Random. Mathematics. 10.3390/math13111728

7. Gao, X., Mesev, V., Li, Z., and Downs, J.  SAPC: A new overland flow accumulation algorithm with enhanced adaptability to terrain surface variations. Transactions in GIS. 10.1111/tgis.70054

8. Foroutan, E., Hu, T. & Li, Z. (2025). Revealing Key Factors of Heat-related Illnesses using Geospatial Explainable AI Model: A Case Study in Texas, USA. Sustainable Cities and Society. 10.1016/j.scs.2025.106243

9. Deng, Rui†., Li., Z. & Wang, M. (2025). GeoAggregator: An Efficient Transformer Model for Geo-spatial Tabular Data. 39th Annual AAAI Conference on Artificial Intelligence (AAAI). https://ojs.aaai.org/index.php/AAAI/article/view/33259/ 

10. Yuan†, X., Li, Z., Basiri, A., & Wang, M. (2025). Construction enthusiasts versus demolition giants: Insights from building footprint data in England. Environment and Planning B: Urban Analytics and City Science, 23998083251317573.

2024

11. Li, Z. Regression Fundamentals (2024). The Geographic Information Science & Technology Body of Knowledge, John P. Wilson (Ed). doi: 10.22224/gistbok/2024.1.11

12. Zhang, Z., Li, Z. & Song, Y. (2024). On ignoring the heterogeneity in spatial autocorrelation: consequences and solutions. International Journal of Geographical Information Science.

13. 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.  Link

14. Zhang, S.†, and Li, Z. (2024). Multilevel Geographical Process Models (MGPMs): A Novel Framework for Modeling Individual- and Multi-Level Spatial Process Heterogeneity. The 27th Association of Geographic Information Laboratories in Europe (AGILE), Glasgow, UK. doi:10.5194/agile-giss-5-55-2024. 

2023

15. 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

16. 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

17. 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.

18. 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

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

20. 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.

2022

21. 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)

22. 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

23. 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

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

25. 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.

26. Li, Z.* & Xu, T†.  (2022). 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

2021

27. 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

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

29. 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

30. 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

31. 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

2020

32. 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.

33. 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)

34. 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)

35. 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.

2019

36. 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

37. 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

38. 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)

39. 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 

40. 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)

2018 and earlier

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

42. 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