Comparer des méthodes

	Autocorrélation spatiale robuste	Autocorrélation spatiale
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1981–1995	1950
Auteur d'origine	Cliff & Ord; extended by Anselin and colleagues	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Type	Spatial dependence test (robust variant)	Spatial statistic / exploratory spatial data analysis
Source fondatrice	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.	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Alias	robust Moran's I, robust spatial dependence test, outlier-resistant spatial autocorrelation, RSA	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Apparentées	5	5
Résumé	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.	Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations.