Comber, Alexis and Harris, Paul and Brunsdon, Chris (2024) Multiscale spatially varying coefficient modelling using a Geographical Gaussian Process GAM. International Journal of Geographical Information Science, 38 (1). pp. 27-47. ISSN 1365-8816

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Abstract

This paper proposes a novel spatially varying coefficient (SVC) regression through a Geographical Gaussian Process GAM (GGP-GAM): a Generalized Additive Model (GAM) with Gaussian Process (GP) splines parameterised at observation locations. A GGP-GAM was applied to multiple simulated coefficient datasets exhibiting varying degrees of spatial heterogeneity and out-performed the SVC brand-leader, Multiscale Geographically Weighted Regression (MGWR), under a range of fit metrics. Both were then applied to a Brexit case study and compared, with MGWR marginally out-performing GGP-GAM. The theoretical frameworks and implementation of both approaches are discussed: GWR models calibrate multiple models whereas GAMs provide a full single model; GAMs can automatically penalise local collinearity; GWR-based approaches are computationally more demanding; MGWR is still only for Gaussian responses; MGWR bandwidths are intuitive indicators of spatial heterogeneity. GGP-GAM calibration and tuning are also discussed and areas of future work are identified, including the creation of a user-friendly package to support model creation and coefficient mapping, and to facilitate ease of comparison with alternate SVC models. A final observation that GGP-GAMs have the potential to overcome some of the long-standing reservations about GWR-based regression methods and to elevate the perception of SVCs amongst the broader community.

Item Type:	Article
Keywords:	Spatial regression; GWR;
Academic Unit:	Faculty of Social Sciences > Geography
Item ID:	18726
Identification Number:	https://doi.org/10.1080/13658816.2023.2270285
Depositing User:	Prof. Chris Brunsdon
Date Deposited:	11 Jul 2024 13:21
Journal or Publication Title:	International Journal of Geographical Information Science
Publisher:	Taylor & Francis
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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