Oshan, Taylor | CGIS l Center for Geospatial Information Science l University of Maryland

Primary Web Profile: on Geographical Sciences Department (geog.umd.edu)

Degrees

Degree Type

BA

Degree Details

History, University of Delaware
Degree Type

MA

Degree Details

Geography, CUNY Hunter College
Degree Type

Ph.D

Degree Details

Geography, Arizona State University

Li, Z., Fotheringham, A. S., Oshan, T. M., & Wolf, L. J. (2020). Measuring Bandwidth Uncertainty in Multiscale Geographically Weighted Regression Using Akaike Weights. The Annals of the American Association of Geographers.
Kang, W., T. Oshan, L. J. Wolf, G. Boeing, A. Poorthuis, S. Gao, W. Xu, V. Frias-Martinez (2019). “A Roundtable Discussion: Defining Urban Data Science”, Environment and Planning, B: Urban Analytics and City Science 6(9): 1756–1768
Oshan, Taylor, Ziqi Li, Wei Kang, Levi Wolf, and Stewart Fotheringham. (2019). “mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale.”
Oshan, Taylor, Levi Wolf, Stewart Fotheringham, Wei Kang, Ziqi Li, and Hanchen Yu. (2019). “A comment on geographically weighted regression with parameter-specific distance metrics”. International Journal of Geographical Information Science 33:7,1289-1299
Yu, Hanchen, A. Stewart Fotheringham, Ziqi Li, Taylor Oshan, Wei Kang, and Levi Wolf. (2019). “Inference in Multi-scale Geographically Weighted Regression.” Geographical Analysis.
Ziqi Li, A. Stewart Fotheringham, Wenwen Li & Taylor Oshan (2019) “Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations”,

1129 Lefrak Hall

Center for Geospatial Information Science

Email

toshan [at] umd.edu