Salīdzināt metodes

	Telpiskās regresijas laika un telpas analīze	Telpiskā laika Kriginga
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1990s–2000s	1999
Autors	Anselin, LeSage, Pace and colleagues in spatial econometrics	Cressie & Huang; Kyriakidis & Journel
Tips	Spatio-temporal regression model	Geostatistical interpolation
Pirmavots	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	Cressie, N., & Huang, H.-C. (1999). Classes of nonseparable, spatio-temporal stationary covariance functions. Journal of the American Statistical Association, 94(448), 1330-1340.
Citi nosaukumi	spatio-temporal regression, spatial panel regression, space-time regression, ST spatial regression	spatiotemporal kriging, ST-kriging, space-time geostatistical interpolation, kriging in space-time
Saistītās	6	4
Kopsavilkums	Space-Time Spatial Regression extends classical spatial regression to panel settings where georeferenced units are observed across multiple time periods. By embedding a spatial weights matrix into a panel regression framework, it simultaneously controls for spatial dependence among cross-sectional units and temporal dynamics, yielding unbiased and consistent estimates in spatio-temporal data.	Space-Time Kriging is a geostatistical interpolation method that predicts an unknown variable at any location and time by borrowing strength from nearby observations in both space and time simultaneously. It models the joint spatial-temporal covariance structure through a space-time variogram, then uses optimal linear weights to produce predictions with quantified uncertainty.