Σύγκριση μεθόδων

	Πολυεπίπεδη Χωρική Αυτοσυσχέτιση	Παλινδρόμηση Πολλαπλής Κλίμακας με Γεωγραφική Στάθμιση (MGWR)
Πεδίο	Χωρική Ανάλυση	Χωρική Ανάλυση
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2002	2017
Δημιουργός	Borcard & Legendre; Csillag & Kabos	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Τύπος	Spatial autocorrelation decomposition	Local spatial regression
Θεμελιώδης πηγή	Borcard, D., & Legendre, P. (2002). All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecological Modelling, 153(1-2), 51-68.	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
Εναλλακτικές ονομασίες	multi-scale spatial autocorrelation, scale-decomposed spatial autocorrelation, multiscale Moran analysis, MSA	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Συναφείς	6	5
Σύνοψη	Multiscale spatial autocorrelation extends classical spatial autocorrelation analysis by computing and comparing autocorrelation statistics (such as Moran's I) across a range of spatial scales simultaneously. This reveals at which geographic distances or resolutions spatial clustering or dispersion is strongest, providing a richer picture than a single global measure.	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.