Salīdzināt metodes

	Telp-laika kodoldensitivitātes novērtēšana (ST-KDE)	Telpiskās Autokorelācijas Paplašinājums Laikā un Telpā
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	2010 (space-time extension); 1956 (KDE origin)	1981–1992
Autors	Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen	Cliff & Ord; extended by Anselin and others
Tips	Non-parametric density estimation	Spatial autocorrelation statistic
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.	Clifford, P., Richardson, S., & Hemon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 45(1), 123–134.
Citi nosaukumi	ST-KDE, spatiotemporal kernel density estimation, space-time KDE, 3D kernel density estimation	STSA, spatiotemporal autocorrelation, space-time Moran's I, temporal spatial dependence
Saistītās	5	5
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.	Space-Time Spatial Autocorrelation extends classic spatial autocorrelation measures — most notably Moran's I — to data that vary across both geographic units and time periods. It detects whether nearby locations that are also temporally close tend to share similar attribute values, revealing clusters, trends, or anomalies that purely spatial or purely temporal analyses would miss.