Methoden vergelijken

	Ruimte-Tijd Kernel Dichtheidsschatting (ST-KDE)	Lokale Kernel Dichtheidsschatting
Vakgebied	Ruimtelijke analyse	Ruimtelijke analyse
Familie	Regression model	Regression model
Jaar van ontstaan	2010 (space-time extension); 1956 (KDE origin)	1985-1986
Grondlegger	Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen	Silverman, B. W.; Diggle, P. J.
Type	Non-parametric density estimation	Non-parametric density estimator
Oorspronkelijke bron	Nakaya, T., & Yano, K. (2010). Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Transactions in GIS, 14(3), 223-239.	Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. ISBN: 978-0412246203
Aliassen	ST-KDE, spatiotemporal kernel density estimation, space-time KDE, 3D kernel density estimation	Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation
Verwant	5	5
Samenvatting	Space-Time Kernel Density Estimation extends classical KDE into three dimensions — two spatial and one temporal — to reveal how the intensity of point events (crimes, accidents, disease cases) varies continuously across both geographic space and time. It produces a smooth probabilistic surface that highlights where and when events concentrate most densely.	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.