Salīdzināt metodes

	Robustā telpiskā autokorelācija	Moran's I
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1981–1995	1950
Autors	Cliff & Ord; extended by Anselin and colleagues	Patrick A. P. Moran
Tips	Spatial dependence test (robust variant)	Spatial autocorrelation statistic
Pirmavots	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.
Citi nosaukumi	robust Moran's I, robust spatial dependence test, outlier-resistant spatial autocorrelation, RSA	Moran's I statistic, global Moran's I, spatial autocorrelation index, Moran index
Saistītās	5	6
Kopsavilkums	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.	Moran's I is the standard global statistic for detecting spatial autocorrelation: whether nearby locations tend to share similar values. The index ranges from approximately −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering), allowing researchers to test whether a geographic pattern differs from complete spatial randomness with a single, interpretable number.