Salīdzināt metodes

	Daudzmērogu telpiskā autokorelācija	Lokālā telpiskā autokorelācija
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	2002	1995
Autors	Borcard & Legendre; Csillag & Kabos	Luc Anselin
Tips	Spatial autocorrelation decomposition	Spatial association analysis
Pirmavots	Borcard, D., & Legendre, P. (2002). All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecological Modelling, 153(1-2), 51-68.	Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.
Citi nosaukumi	multi-scale spatial autocorrelation, scale-decomposed spatial autocorrelation, multiscale Moran analysis, MSA	local spatial association, local SA, LISA methods, local spatial clustering
Saistītās	6	6
Kopsavilkums	Multiscale spatial autocorrelation extends classical spatial autocorrelation analysis by computing and comparing autocorrelation statistics (such as Moran's I) across a range of spatial scales simultaneously. This reveals at which geographic distances or resolutions spatial clustering or dispersion is strongest, providing a richer picture than a single global measure.	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.