Comparer des méthodes

	Estimation locale par noyau de la densité	Analyse spatiale basée sur le réseau
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1985-1986	1990s–2000s
Auteur d'origine	Silverman, B. W.; Diggle, P. J.	Atsuyuki Okabe and colleagues
Type	Non-parametric density estimator	Spatial network model
Source fondatrice	Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. ISBN: 978-0412246203	Okabe, A., Satoh, T., Furuta, T., Sugihara, K., & Okano, K. (2006). Generalized network Voronoi diagrams: Concepts, computational methods, and applications. International Journal of Geographical Information Science, 22(9), 965–994.
Alias	Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation	network spatial analysis, network-constrained spatial analysis, spatial network analysis, NBSA
Apparentées	5	3
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.	Network-based spatial analysis (NBSA) analyzes the distribution and interaction of spatial phenomena constrained to a network structure — such as roads, railways, or rivers — using network distance rather than straight-line (Euclidean) distance. It is the appropriate framework whenever movement, proximity, or risk is governed by the underlying network topology rather than open space.