Compară metode

	Estimarea locală a densității prin nucleu	Analiză spațială bazată pe rețea
Domeniu	Analiză spațială	Analiză spațială
Familie	Regression model	Regression model
Anul apariției	1985-1986	1990s–2000s
Autorul original	Silverman, B. W.; Diggle, P. J.	Atsuyuki Okabe and colleagues
Tip	Non-parametric density estimator	Spatial network model
Sursa seminală	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.
Denumiri alternative	Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation	network spatial analysis, network-constrained spatial analysis, spatial network analysis, NBSA
Înrudite	5	3
Rezumat	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.