Vertaile menetelmiä

	Robust spatial autocorrelation	Paikallinen spatiaalinen autokorrelaatio
Tieteenala	Spatiaalianalyysi	Spatiaalianalyysi
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1981–1995	1995
Kehittäjä	Cliff & Ord; extended by Anselin and colleagues	Luc Anselin
Tyyppi	Spatial dependence test (robust variant)	Spatial association analysis
Alkuperäislähde	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.
Rinnakkaisnimet	robust Moran's I, robust spatial dependence test, outlier-resistant spatial autocorrelation, RSA	local spatial association, local SA, LISA methods, local spatial clustering
Liittyvät	5	6
Tiivistelmä	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.