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 in GIS
- GOEG 595 - Advanced Studies in Geography
Fotheringham, A. S., Li, Z., & Wolf, L. J. (forthcoming). Scale, context and heterogeneity: A spatial analytical perspective on the 2016 US presidential election. Annals of the American Association of Geographers.
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.
Li, Z. & Fotheringham, A. S. (2020).Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science. 34(7), 1378-1397.
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.
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.
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.
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.
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.
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.