Compara mètodes

	Estimació de Densitat del Nucli Local	Auto-correlació espacial local
Camp	Anàlisi espacial	Anàlisi espacial
Família	Regression model	Regression model
Any d'origen	1985-1986	1995
Autor original	Silverman, B. W.; Diggle, P. J.	Luc Anselin
Tipus	Non-parametric density estimator	Spatial association analysis
Font seminal	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.
Àlies	Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation	local spatial association, local SA, LISA methods, local spatial clustering
Relacionats	5	6
Resum	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.