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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

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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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