Salīdzināt metodes

	Telp-laika kodoldensitivitātes novērtēšana (ST-KDE)	Getis-Ord Gi* statistika laika un telpā
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	2010 (space-time extension); 1956 (KDE origin)	1992 (Gi*); space-time extension ~2000s–2010s
Autors	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
Tips	Non-parametric density estimation	Local spatial statistic (space-time extension)
Pirmavots	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.
Citi nosaukumi	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
Saistītās	5	4
Kopsavilkums	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.