Comparar métodos

	Estimación de la Densidad de Kernel Local	Local Moran's I (LISA)
Campo	Análisis espacial	Análisis espacial
Familia	Regression model	Regression model
Año de origen	1985-1986	1995
Autor original	Silverman, B. W.; Diggle, P. J.	Luc Anselin
Tipo	Non-parametric density estimator	Local spatial autocorrelation statistic
Fuente 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.
Alias	Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation	Local Indicator of Spatial Association, LISA statistic, Anselin Local Moran, local spatial autocorrelation index
Relacionados	5	6
Resumen	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 Moran's I, introduced by Luc Anselin in 1995, is a Local Indicator of Spatial Association (LISA) that decomposes global spatial autocorrelation into location-specific contributions. For every observation it produces a signed statistic and a significance value, enabling researchers to identify spatial clusters (high-high, low-low) and spatial outliers (high-low, low-high) on a map.