Multiscale spatially varying coefficient modelling using a Geographical Gaussian Process GAM

A - Papers appearing in refereed journals

Comber, A, Harris, P. and Brunsdon, C 2023. Multiscale spatially varying coefficient modelling using a Geographical Gaussian Process GAM. International Journal Of Geographical Information Science.

AuthorsComber, A, Harris, P. and Brunsdon, C

This paper proposes a novel spatially varying coefficient (SVC) regression through a Geographical Gaussian Process GAM (GGPGAM): 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 GWRbased regression methods and to elevate the perception of SVCs amongst the broader community.

KeywordsSpatial regression; GWR
Year of Publication2023
JournalInternational Journal Of Geographical Information Science
Digital Object Identifier (DOI)
Open accessPublished as ‘gold’ (paid) open access
FunderNatural Environment Research Council
Biotechnology and Biological Sciences Research Council
Funder project or codeMIDST-CZ: Maximising Impact by Decision Support Tools for sustainable soil and water through UK-China Critical Zone science
Resilient Farming Futures
The North Wyke Farm Platform- National Capability [2023-28]
Publisher's version
Supplemental file
Output statusPublished
Publication dates
Online27 Oct 2023
Publication process dates
Accepted04 Oct 2023
PublisherTaylor & Francis

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