Módszerek összehasonlítása

	Helyi Ordináris Kriging	Multiscale Geographically Weighted Regression (MGWR)
Tudományterület	Térbeli elemzés	Térbeli elemzés
Módszercsalád	Regression model	Regression model
Keletkezés éve	1970s–1990s	2017
Megalkotó	Journel & Huijbregts; developed further by Goovaerts and Chiles & Delfiner	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Típus	Geostatistical interpolation (local/moving-window variant)	Local spatial regression
Alapmű	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.
Alternatív nevek	moving window kriging, local kriging, neighborhood kriging, LOK	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Kapcsolódó	5	5
Összefoglaló	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.