Urbana, IL 61801
My research interests include spatial statistical and computing methods, spatial data science, and the application of these techniques in multidisciplinary fields. I received my PhD from Arizona State University, and my dissertation focused on the Multi-scale Geographically Weighted Regression (MGWR) model. I'm currently working on developing spatially explicit statistical learning models. In addition, I advocate for open and reproducible science. I'm the developer of mgwr python package and several other open-source software.
- Ph.D., Geography, Arizona State University
- M.A., Geography, George Washington University
- B.S., Geomatics, University of Waterloo, Canada
- B.Eng., Remote Sensing, Wuhan University, China
- GEOG 480 - Principles of GIS
- GOEG 595 - Advanced Studies in Geography
Fotheringham, A. S., Li, Z., & Wolf, L. J. (2020). Scale, context and heterogeneity: A spatial analytical perspective on the 2016 US presidential election. Annals of the American Association of Geographers. Link
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.
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. 110(5), 1500-1520. Link
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
Yu, H., Fotheringham, A. S., Li, Z., Oshan, T., & Wolf, L. J. (2020). On the measurement bias of geographically weighted regression models. Spatial Statistics. 38, 100453.
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
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.