2026-07-22T21:25:28Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18917 2024-03-06T11:02:57Z ddc:004 open_access

Multiscale Spatially and Temporally Varying Coefficient Modelling Using a Geographic and Temporal Gaussian Process GAM (GTGP-GAM) (Short Paper) Comber, Alexis Harris, Paul Brunsdon, Chris Spatial Analysis Spatiotemproal Analysis 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Alexis Comber and Paul Harris and Chris Brunsdon 2023 Is Part Of LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2023.22 urn:nbn:de:0030-drops-189173 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.22 eng https://creativecommons.org/licenses/by/4.0/legalcode