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Regression modelGIS / spatial

Multiskal Geografisk Vægtet Regression (MGWR)

Multiskal Geografisk Vægtet Regression (MGWR) er et lokalt rumligt regressionsframework, der løsner enkeltbåndbreddebegrænsningen af standard GWR ved at tillade, at hver prædiktor opererer i sin egen rumlige skala. Hver koefficientoverflade kalibreres med sin egen båndbredde, hvilket gør det muligt for modellen at skelne mellem drivkræfter, der varierer langsomt hen over rummet, og dem, der varierer skarpt.

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Kilder

  1. Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. DOI: 10.1080/24694452.2017.1352480
  2. Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6), 269. DOI: 10.3390/ijgi8060269

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ScholarGate. (2026, June 3). Multiscale Geographically Weighted Regression. ScholarGate. https://scholargate.app/da/spatial-analysis/multiscale-geographically-weighted-regression

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ScholarGateMultiscale Geographically Weighted Regression (Multiscale Geographically Weighted Regression). Hentet 2026-06-15 fra https://scholargate.app/da/spatial-analysis/multiscale-geographically-weighted-regression · Datasæt: https://doi.org/10.5281/zenodo.20539026