Compară metode

	Estimarea Densității Kernel Spațio-Temporale (ST-KDE)	Statistica spațio-temporală Getis-Ord Gi*
Domeniu	Analiză spațială	Analiză spațială
Familie	Regression model	Regression model
Anul apariției	2010 (space-time extension); 1956 (KDE origin)	1992 (Gi*); space-time extension ~2000s–2010s
Autorul original	Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen	Getis & Ord (seminal); space-time extension developed in GIS literature and ArcGIS Emerging Hot Spot Analysis
Tip	Non-parametric density estimation	Local spatial statistic (space-time extension)
Sursa seminală	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.	Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.
Denumiri alternative	ST-KDE, spatiotemporal kernel density estimation, space-time KDE, 3D kernel density estimation	ST-Gi*, space-time hot spot analysis, emerging hot spot analysis, space-time local autocorrelation statistic
Înrudite	5	4
Rezumat	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.	The Space-Time Getis-Ord Gi* statistic extends the classic Gi* local hot spot measure into three dimensions — two spatial and one temporal — revealing not only where concentrations of high or low values cluster, but how those clusters evolve, intensify, or diminish over time. It is widely used in crime analysis, epidemiology, ecology, and urban studies.