Salīdzināt metodes

	Robustā Getisa-Ord Gi* statistika	Robustie lokālie telpiskās asociācijas rādītāji (Robust LISA)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1992 (base); robust variants circa 2000s–2010s	1995–2000s
Autors	Getis & Ord (base statistic); robust extensions developed in subsequent spatial statistics literature	Anselin (LISA, 1995); robust extensions by Assuncao & Reis and subsequent spatial statisticians
Tips	Local spatial statistic	Local spatial autocorrelation statistic (robust variant)
Pirmavots	Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.	Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.
Citi nosaukumi	Robust Gi*, Robust local Gi star, outlier-resistant hot spot analysis, robust local spatial autocorrelation Gi*	Robust LISA, outlier-resistant LISA, robust local spatial autocorrelation, LISA with robust weights
Saistītās	5	6
Kopsavilkums	The Robust Getis-Ord Gi* statistic extends the classical Gi* hot-spot measure to handle outliers in spatial data. By using robust estimators of the mean and variance — such as trimmed means, medians, or down-weighted influential observations — it identifies statistically significant spatial clusters of high or low values even when the attribute distribution contains extreme values that would distort the standard Gi*.	Robust Local Indicators of Spatial Association extend Anselin's LISA framework to handle outliers, extreme values, and spatially heterogeneous populations. By applying outlier-resistant adjustments to the spatial weights or the standardised values, Robust LISA identifies statistically significant local clusters and spatial outliers without the distortions caused by highly influential observations.