Salīdzināt metodes

	Daudzskalu Morana I	Daudzskalu ģeogrāfiski svērtā regresija (MGWR)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1950 (base); multiscale variant 1980s-1990s	2017
Autors	P. A. P. Moran (base statistic, 1950); multiscale extension developed through spatial ecology and geography literature	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Tips	Spatial autocorrelation statistic	Local spatial regression
Pirmavots	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1-2), 17-23.	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
Citi nosaukumi	multi-scale Moran's I, spatial correlogram Moran, Moran correlogram, multiscale spatial autocorrelation	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Saistītās	6	5
Kopsavilkums	Multiscale Moran's I extends the classic global Moran's I statistic by computing spatial autocorrelation across a series of distance lags or spatial scales. The resulting spatial correlogram reveals at which geographic scales clusters or dispersions of a variable exist, offering richer insight than a single summary statistic.	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.