Linganisha mbinu

	Kriging ya Nafasi-Wakati	Uhalali wa Nafasi-Wakati wa Kina
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1999	1981–1992
Mwanzilishi	Cressie & Huang; Kyriakidis & Journel	Cliff & Ord; extended by Anselin and others
Aina	Geostatistical interpolation	Spatial autocorrelation statistic
Chanzo asilia	Cressie, N., & Huang, H.-C. (1999). Classes of nonseparable, spatio-temporal stationary covariance functions. Journal of the American Statistical Association, 94(448), 1330-1340.	Clifford, P., Richardson, S., & Hemon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 45(1), 123–134.
Majina mbadala	spatiotemporal kriging, ST-kriging, space-time geostatistical interpolation, kriging in space-time	STSA, spatiotemporal autocorrelation, space-time Moran's I, temporal spatial dependence
Zinazohusiana	4	5
Muhtasari	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.	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.