Сравнение методов

	Статистика Robust Getis-Ord Gi*	Робастные методы пространственной автокорреляции
Область	Пространственный анализ	Пространственный анализ
Семейство	Regression model	Regression model
Год появления	1992 (base); robust variants circa 2000s–2010s	1981–1995
Автор метода	Getis & Ord (base statistic); robust extensions developed in subsequent spatial statistics literature	Cliff & Ord; extended by Anselin and colleagues
Тип	Local spatial statistic	Spatial dependence test (robust variant)
Основополагающий источник	Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.	Anselin, L., & Florax, R. J. G. M. (1995). Small sample properties of tests for spatial dependence in regression models: some further results. In Anselin, L. & Florax, R. J. G. M. (Eds.), New Directions in Spatial Econometrics. Springer, Berlin.
Другие названия	Robust Gi*, Robust local Gi star, outlier-resistant hot spot analysis, robust local spatial autocorrelation Gi*	robust Moran's I, robust spatial dependence test, outlier-resistant spatial autocorrelation, RSA
Связанные	5	5
Сводка	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 spatial autocorrelation methods measure the degree to which nearby geographic units share similar values, while explicitly controlling for the distorting influence of spatial outliers and extreme observations. They extend classical statistics such as Moran's I by down-weighting or trimming observations that would otherwise inflate or deflate the autocorrelation signal.