Сравнение методов

	Пространственно-временная пространственная регрессия	Пространственно-временной кригинг
Область	Пространственный анализ	Пространственный анализ
Семейство	Regression model	Regression model
Год появления	1990s–2000s	1999
Автор метода	Anselin, LeSage, Pace and colleagues in spatial econometrics	Cressie & Huang; Kyriakidis & Journel
Тип	Spatio-temporal regression model	Geostatistical interpolation
Основополагающий источник	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.
Другие названия	spatio-temporal regression, spatial panel regression, space-time regression, ST spatial regression	spatiotemporal kriging, ST-kriging, space-time geostatistical interpolation, kriging in space-time
Связанные	6	4
Сводка	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.