Geographically Varying Coefficient Regression: GWR-Exit and GAM-On? (Short Paper)

Authors Alexis Comber , Paul Harris , Daisuke Murakami , Narumasa Tsutsumida , Chris Brunsdon

Thumbnail PDF


  • Filesize: 1.54 MB
  • 10 pages

Document Identifiers

Author Details

Alexis Comber
  • University of Leeds, UK
Paul Harris
  • Rothamsted Research, Harpenden, UK
Daisuke Murakami
  • Institute of Statistical Mathematics, Tokyo, Japan
Narumasa Tsutsumida
  • Saitama University, Japan
Chris Brunsdon
  • Maynooth University, Ireland

Cite AsGet BibTex

Alexis Comber, Paul Harris, Daisuke Murakami, Narumasa Tsutsumida, and Chris Brunsdon. Geographically Varying Coefficient Regression: GWR-Exit and GAM-On? (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 13:1-13:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


This paper describes initial work exploring two spatially varying coefficient models: multi-scale GWR and GAM Gaussian Process spline parameterised by observation location. Both approaches accommodate process spatial heterogeneity and both generate outputs that can be mapped indicating the nature of the process heterogeneity. However the nature of the process heterogeneity they each describe are very different. This suggests that the underlying semantics of such models need to be considered in order to refine the specificity of the questions that are asked of data: simply seeking to understand process spatial heterogeneity may be too semantically coarse.

Subject Classification

ACM Subject Classification
  • Information systems
  • Theory of computation
  • Geographically weighted regression
  • Spatial Analysis
  • Process Spatial Heterogeneity
  • Model Semantics


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads


  1. Chris Brunsdon and Alexis Comber. Opening practice: supporting reproducibility and critical spatial data science. Journal of Geographical Systems, 23(4):477-496, 2021. Google Scholar
  2. Chris Brunsdon, A Stewart Fotheringham, and Martin E Charlton. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4):281-298, 1996. Google Scholar
  3. Chris Brunsdon, Stewart Fotheringham, and Martin Charlton. Geographically weighted discriminant analysis. Geographical Analysis, 39(4):376-396, 2007. Google Scholar
  4. Alexis Comber, Chris Brunsdon, Martin Charlton, and Paul Harris. Geographically weighted correspondence matrices for local error reporting and change analyses: mapping the spatial distribution of errors and change. Remote Sensing Letters, 8(3):234-243, 2017. Google Scholar
  5. Alexis Comber, Christopher Brunsdon, Martin Charlton, Guanpeng Dong, Richard Harris, Binbin Lu, Yihe Lü, Daisuke Murakami, Tomoki Nakaya, Yunqiang Wang, et al. A route map for successful applications of geographically weighted regression. Geographical Analysis, 0:1-24, 2022. Google Scholar
  6. Alexis Comber, Nick Malleson, Hang Nguyen Thi Thuy, Thanh Bui Quang, Minh Kieu, Hoang Huu Phe, and Paul Harris. Multiscale geographically weighted discriminant analysis. In GIScience 2021 Short Paper Proceedings, pages 17-32. Springer, 2021. URL:
  7. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, and Brian D Marx. Regression models. In Regression, pages 23-84. Springer, 2021. Google Scholar
  8. A Stewart Fotheringham, Wenbai Yang, and Wei Kang. Multiscale geographically weighted regression (mgwr). Annals of the American Association of Geographers, 107(6):1247-1265, 2017. Google Scholar
  9. Alan E Gelfand and Dipak K Dey. Bayesian model choice: asymptotics and exact calculations. Journal of the Royal Statistical Society: Series B (Methodological), 56(3):501-514, 1994. Google Scholar
  10. Paul Harris, Chris Brunsdon, and Martin Charlton. Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10):1717-1736, 2011. Google Scholar
  11. Trevor J Hefley, Kristin M Broms, Brian M Brost, Frances E Buderman, Shannon L Kay, Henry R Scharf, John R Tipton, Perry J Williams, and Mevin B Hooten. The basis function approach for modeling autocorrelation in ecological data. Ecology, 98(3):632-646, 2017. Google Scholar
  12. Stan Openshaw. Developing gis-relevant zone-based spatial analysis methods. Spatial analysis: modelling in a GIS environment, pages 55-73, 1996. Google Scholar
  13. Taylor M Oshan, Ziqi Li, Wei Kang, Levi J Wolf, and A Stewart Fotheringham. 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, 2019. Google Scholar
  14. PD Sampson, D Damian, and P Guttorp. Advances in modeling and inference for environmental processes with nonstationary spatial covariance. In GeoENV III - Geostatistics for Environmental Applications, pages 17-32. Springer, 2001. Google Scholar
  15. Waldo R Tobler. A computer movie simulating urban growth in the detroit region. Economic geography, 46(sup1):234-240, 1970. Google Scholar
  16. Levi John Wolf, Taylor M Oshan, and A Stewart Fotheringham. Single and multiscale models of process spatial heterogeneity. Geographical Analysis, 50(3):223-246, 2018. Google Scholar
  17. Simon N Wood. Generalized additive models: an introduction with R. chapman and hall/CRC, 2006. Google Scholar
  18. Wenbai Yang. An extension of geographically weighted regression with flexible bandwidths. PhD thesis, University of St Andrews, 2014. Google Scholar
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail