Comparer des méthodes

	Estimation locale par noyau de la densité	Autocorrélation spatiale locale
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1985-1986	1995
Auteur d'origine	Silverman, B. W.; Diggle, P. J.	Luc Anselin
Type	Non-parametric density estimator	Spatial association analysis
Source fondatrice	Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. ISBN: 978-0412246203	Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.
Alias	Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation	local spatial association, local SA, LISA methods, local spatial clustering
Apparentées	5	6
Résumé	Local Kernel Density Estimation (Local KDE) is a non-parametric spatial method that estimates the density of point events at each location by applying a kernel function with a spatially adaptive bandwidth. Unlike global KDE, which uses a fixed bandwidth across the entire study area, Local KDE adjusts the smoothing window according to local data density, capturing fine-scale clustering where events are sparse or concentrated.	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.