Salīdzināt metodes

	Daudzskalu Getis-Ord Gi* karstā punkta analīze	Daudzskalu ģeogrāfiski svērtā regresija (MGWR)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1995 (Gi* basis); multiscale application 2000s onward	2017
Autors	Ord & Getis (1995); multiscale extension developed in applied spatial analysis practice	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Tips	Local spatial statistic (multiscale)	Local spatial regression
Pirmavots	Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286-306.	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-distance Gi*, multiscale hot spot analysis, multi-bandwidth Getis-Ord, scale-varying Gi*	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Saistītās	5	5
Kopsavilkums	Multiscale Getis-Ord Gi* extends the classic local hot spot statistic by computing Gi* z-scores across a range of spatial distance bands or neighborhood sizes. This reveals whether clusters of high or low values are scale-dependent — appearing only at fine local scales, only at broad regional scales, or persistently across all scales — providing richer spatial intelligence than a single-bandwidth analysis.	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.