Taylor M. Oshan
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MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale
TM Oshan, Z Li, W Kang, LJ Wolf, AS Fotheringham
ISPRS International Journal of Geo-Information 8 (6), 269, 2019
Analysis of human mobility patterns from GPS trajectories and contextual information
K Siła-Nowicka, J Vandrol, T Oshan, JA Long, U Demšar, ...
International Journal of Geographical Information Science 30 (5), 881-906, 2016
Geographically weighted regression and multicollinearity: dispelling the myth
AS Fotheringham, TM Oshan
Journal of Geographical Systems 18 (4), 303-329, 2016
Inference in multiscale geographically weighted regression
H Yu, AS Fotheringham, Z Li, T Oshan, W Kang, LJ Wolf
Geographical Analysis 52 (1), 87-106, 2020
Targeting the spatial context of obesity determinants via multiscale geographically weighted regression
TM Oshan, J Smith, AS Fotheringham
OSF Preprints, 2020
Single and Multiscale Models of Process Spatial Heterogeneity
LJ Wolf, TM Oshan, AS Fotheringham
Geographical Analysis, 2018
Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations
Z Li, AS Fotheringham, W Li, T Oshan
International Journal of Geographical Information Science 33 (1), 155-175, 2019
A comparison of spatially varying regression coefficient estimates using geographically weighted and spatial‐filter‐based techniques
TM Oshan, AS Fotheringham
Geographical Analysis 50 (1), 53-75, 2018
Measuring bandwidth uncertainty in multiscale geographically weighted regression using Akaike weights
Z Li, AS Fotheringham, TM Oshan, LJ Wolf
Annals of the American Association of Geographers 110 (5), 1500-1520, 2020
A comment on geographically weighted regression with parameter-specific distance metrics
T Oshan, LJ Wolf, AS Fotheringham, W Kang, Z Li, H Yu
International Journal of Geographical Information Science 33 (7), 1289-1299, 2019
A primer for working with the Spatial Interaction modeling (SpInt) module in the python spatial analysis library (PySAL)
TM Oshan
Region 3 (2), R11-R23, 2016
On the measurement of bias in geographically weighted regression models
H Yu, AS Fotheringham, Z Li, T Oshan, LJ Wolf
Spatial Statistics 38, 100453, 2020
The spatial structure debate in spatial interaction modeling: 50 years on
TM Oshan
Progress in Human Geography 45 (5), 925-950, 2021
A roundtable discussion: Defining urban data science
Organizers, W Kang, T Oshan, LJ Wolf, Discussants, G Boeing, ...
Environment and Planning B: Urban Analytics and City Science 46 (9), 1756-1768, 2019
The PySAL Ecosystem: Philosophy and Implementation
SJ Rey, L Anselin, P Amaral, D Arribas‐Bel, RX Cortes, JD Gaboardi, ...
Geographical Analysis 54 (3), 467-487, 2022
Spatial interaction
C Farmer, T Oshan
The Geographic Information Science & Technology Body of Knowledge (4th…, 2017
Potential and pitfalls of big transport data for spatial interaction models of urban mobility
TM Oshan
The Professional Geographer 72 (4), 468-480, 2020
Demš ar U, Fotheringham AS
K Sia-Nowicka, J Vandrol, T Oshan, JA Long
Int J Geogr Inf Sci 30 (5), 881, 2016
The Importance of Null Hypotheses: Understanding Differences in Local Moran’s under Heteroskedasticity
J Sauer, T Oshan, S Rey, LJ Wolf
Geographical Analysis 54 (4), 752-768, 2022
On the notion of ‘bandwidth’in geographically weighted regression models of spatially varying processes
AS Fotheringham, H Yu, LJ Wolf, TM Oshan, Z Li
International Journal of Geographical Information Science, 1-18, 2022
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