Salīdzināt metodes

	Robustā telpiskā autokorelācija	Lokālā telpiskā autokorelācija
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1981–1995	1995
Autors	Cliff & Ord; extended by Anselin and colleagues	Luc Anselin
Tips	Spatial dependence test (robust variant)	Spatial association analysis
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.	Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.
Citi nosaukumi	robust Moran's I, robust spatial dependence test, outlier-resistant spatial autocorrelation, RSA	local spatial association, local SA, LISA methods, local spatial clustering
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.	Local Spatial Autocorrelation methods decompose global spatial clustering into location-specific statistics, revealing where in a study area significant clustering or dispersion occurs. Each observation receives its own association score and significance value, enabling the detection of spatial hot spots, cold spots, and spatial outliers rather than reporting a single summary statistic.