Confronta i metodi

	Kriging Ordinario Locale	Regressione Pesata Geograficamente Multiscala (MGWR)
Campo	Analisi spaziale	Analisi spaziale
Famiglia	Regression model	Regression model
Anno di origine	1970s–1990s	2017
Ideatore	Journel & Huijbregts; developed further by Goovaerts and Chiles & Delfiner	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Tipo	Geostatistical interpolation (local/moving-window variant)	Local spatial regression
Fonte seminale	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.
Alias	moving window kriging, local kriging, neighborhood kriging, LOK	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Correlati	5	5
Sintesi	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.