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Multiscale Spatially and Temporally Varying Coefficient Modelling Using a Geographic and Temporal Gaussian Process GAM (GTGP-GAM) (Short Paper)

Authors Alexis Comber , Paul Harris , Chris Brunsdon

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Author Details

Alexis Comber
  • School of Geography, University of Leeds, UK
Paul Harris
  • Sustainable Agriculture Sciences, Rothamsted Research, Harpenden, UK
Chris Brunsdon
  • National Centre for Geomcomputation, National University of Ireland, Maynooth, Ireland

Funding

This work was supported by the JSPS BRIDGE fellowship No. 220305.

Cite As Get BibTex

Alexis Comber, Paul Harris, and Chris Brunsdon. Multiscale Spatially and Temporally Varying Coefficient Modelling Using a Geographic and Temporal Gaussian Process GAM (GTGP-GAM) (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 22:1-22:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.GIScience.2023.22

Abstract

The paper develops a novel approach to spatially and temporally varying coefficient (STVC) modelling, using Generalised Additive Models (GAMs) with Gaussian Process (GP) splines parameterised with location and time variables - a Geographic and Temporal Gaussian Process GAM (GTGP-GAM). This was applied to a Mongolian livestock case study and different forms of GTGP splines were evaluated in which space and time were combined or treated separately. A single 3-D spline with rescaled temporal and spatial attributes resulted in the best model under an assumption that for spatial and temporal processes interact a case studies with a sufficiently large spatial extent is needed. A fully tuned model was then created and the spline smoothing parameters were shown to indicate the degree of variation in covariate spatio-temporal interactions with the target variable.

Subject Classification

ACM Subject Classification
  • Information systems → Spatial-temporal systems
Keywords
  • Spatial Analysis
  • Spatiotemproal Analysis

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References

  1. Alexis Comber, Paul Harris, and Chris Brunsdon. Multiscale spatially varying coefficient modelling using a geographical gaussian process gam. International Journal of Geographical Information Science, submitted. Google Scholar
  2. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, and Brian D Marx. Regression models. In Regression, pages 23-84. Springer, 2021. Google Scholar
  3. John McPeak. Confronting the risk of asset loss: What role do livestock transfers in northern kenya play? Journal of Development Economics, 81(2):415-437, 2006. Google Scholar
  4. Stan Openshaw. Developing gis-relevant zone-based spatial analysis methods. Spatial analysis: modelling in a GIS environment, pages 55-73, 1996. Google Scholar
  5. Narumasa Tsutsumida, Paul Harris, and Alexis Comber. The application of a geographically weighted principal component analysis for exploring twenty-three years of goat population change across mongolia. Annals of the American Association of Geographers, 107(5):1060-1074, 2017. Google Scholar
  6. Simon N Wood. Generalized additive models: an introduction with R. Chapman Hall/CRC, 2006. Google Scholar
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