Bandingkan metode

	Estimasi Kepadatan Kernel Lokal	Autokorelasi Spasial
Bidang	Analisis Spasial	Analisis Spasial
Keluarga	Regression model	Regression model
Tahun asal	1985-1986	1950
Pencetus	Silverman, B. W.; Diggle, P. J.	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Tipe	Non-parametric density estimator	Spatial statistic / exploratory spatial data analysis
Sumber perintis	Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. ISBN: 978-0412246203	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Alias	Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Terkait	5	5
Ringkasan	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.	Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations.