Oshan, T. M., & Liao, M. (2024). Generalized additive spatial smoothing (GASS): A multiscale regression framework for modeling neighborhood effects across spatial supports.

Fotheringham, A.S., Oshan, T.M., & Li, Ziqi (2023). Multiscale Geographically Weighted Regression: Theory and Practice. Routledge CRC Press.

Oshan, T.M. (2023), Navigating the Methodological Landscape in Spatial Analysis: A Comment on “A Route Map for Successful Applications of Geographically-Weighted Regression”. Geographical Analysis.

Oshan, T.M., Wolf, L.J., Sachdeva, M. et al. A scoping review on the multiplicity of scale in spatial analysis. J Geogr Syst (2022).

Liu, Z., & Oshan, T. (2022). Comparing spatial interaction models and flow interpolation techniques for predicting “cold start” bike-share trip demand. Transactions in GIS, 00, 1– 18.

A. Stewart Fotheringham, Hanchen Yu, Levi John Wolf, Taylor M. Oshan & Ziqi Li (2022) On the notion of ‘bandwidth’ in geographically weighted regression models of spatially varying processes, International Journal of Geographical Information Science

Sauer, J., Oshan, T., Rey, S., & Wolf, L. J. (2021). The Importance of Null Hypotheses: Understanding Differences in Local Moran’s under Heteroskedasticity. Geographical Analysis.

Rey, S. J., Anselin, L., Amaral, P., Arribas-Bel, D., Cortes, R. X., Gaboardi, J. D., Kang, W., Knaap, E., Li, Z., Lumnitz, S., Oshan, T. M., Shao, H., & Wolf, L. J. (2021). The PySAL Ecosystem: Philosophy and Implementation. Geographical Analysis.

Park, S., Oshan, T. M., El Ali, A., & Finamore, A. (2021). Are we breaking bubbles as we move? Using a large sample to explore the relationship between urban mobility and segregation. Computers, Environment and Urban Systems, 86, 101585.

Oshan, T. M. (2020). The spatial structure debate in spatial interaction modeling: 50 years on. Progress in Human Geography, 0309132520968134.

Oshan, T. M. (2020). Potential and Pitfalls of Big Transport Data for Spatial Interaction Models of Urban Mobility. The Professional Geographer, 72(4), 468–480.

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, 38, 100453.

Oshan, T. M., Smith, J. P., & Fotheringham, A. S. (2020). Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. International Journal of Health Geographics, 19(1), 11.

Li, Z., Fotheringham, A. S., Oshan, T. M., & 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.

Yu, H., Fotheringham, A. S., Li, Z., Oshan, T., Kang, W., & Wolf, L. J. (2020). Inference in Multiscale Geographically Weighted Regression. Geographical Analysis, 52(1), 87–106.

Kang, W., Oshan, T., Wolf, L. J., Boeing, G., Frias-Martinez, V., Gao, S., Poorthuis, A., & Xu, W. (2019). A roundtable discussion: Defining urban data science. Environment and Planning B: Urban Analytics and City Science, 46(9), 1756–1768.

Oshan, T., Li, Z., Kang, W., Wolf, L., & Fotheringham, A. (2019). mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. ISPRS International Journal of Geo-Information, 8(6), 269.

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.

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.

Oshan, T. M., & Fotheringham, A. S. (2018). A Comparison of Spatially Varying Regression Coefficient Estimates Using Geographically Weighted and Spatial-Filter-Based Techniques. Geographical Analysis, 50(1), 53–75.

Wolf, L. J., Oshan, T. M., & Fotheringham, A. S. (2018). Single and Multiscale Models of Process Spatial Heterogeneity. Geographical Analysis, 50(3), 223–246.

Farmer, C. and Oshan, T. (2017). Spatial interaction. The Geographic Information Science & Technology Body of Knowledge (4th Quarter 2017 Edition), John P. Wilson (ed.).

Oshan, T. M. (2016). A primer for working with the Spatial Interaction modeling (SpInt) module in the python spatial analysis library (PySAL). REGION, 3(2), 11.

Fotheringham, A. S., & Oshan, T. M. (2016). Geographically weighted regression and multicollinearity: Dispelling the myth. Journal of Geographical Systems, 18(4), 303–329.

Siła-Nowicka, K., Vandrol, J., Oshan, T., Long, J. A., Demšar, U., & Fotheringham, A. S. (2016). Analysis of human mobility patterns from GPS trajectories and contextual information. International Journal of Geographical Information Science, 30(5), 881–906.