Salīdzināt metodes

	Robustie lokālie telpiskās asociācijas rādītāji (Robust LISA)	Robustā telpiskā autokorelācija
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1995–2000s	1981–1995
Autors	Anselin (LISA, 1995); robust extensions by Assuncao & Reis and subsequent spatial statisticians	Cliff & Ord; extended by Anselin and colleagues
Tips	Local spatial autocorrelation statistic (robust variant)	Spatial dependence test (robust variant)
Pirmavots	Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.	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.
Citi nosaukumi	Robust LISA, outlier-resistant LISA, robust local spatial autocorrelation, LISA with robust weights	robust Moran's I, robust spatial dependence test, outlier-resistant spatial autocorrelation, RSA
Saistītās	6	5
Kopsavilkums	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.	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.