Methoden vergleichen

	Lokale Ordinary Kriging-Interpolation	Multiskalige Geographisch Gewichtete Regression (MGWR)
Fachgebiet	Räumliche Analyse	Räumliche Analyse
Familie	Regression model	Regression model
Entstehungsjahr	1970s–1990s	2017
Urheber	Journel & Huijbregts; developed further by Goovaerts and Chiles & Delfiner	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Typ	Geostatistical interpolation (local/moving-window variant)	Local spatial regression
Wegweisende Quelle	Chiles, J.-P., & Delfiner, P. (1999). Geostatistics: Modeling Spatial Uncertainty. Wiley. ISBN: 978-0471083153	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
Aliasnamen	moving window kriging, local kriging, neighborhood kriging, LOK	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Verwandt	5	5
Zusammenfassung	Local Ordinary Kriging (LOK) is a geostatistical interpolation method that estimates values at unsampled locations using only a spatially defined moving neighborhood of nearby observations. By restricting each prediction to a local data window rather than the full dataset, LOK accommodates spatial non-stationarity, reduces computational cost, and often yields more accurate local predictions than global ordinary kriging.	Multiscale Geographically Weighted Regression (MGWR) is a local spatial regression framework that relaxes the single-bandwidth constraint of standard GWR by allowing each predictor to operate at its own spatial scale. Each coefficient surface is calibrated with its own bandwidth, enabling the model to distinguish drivers that vary slowly across space from those that vary sharply.