Salīdzināt metodes

	Telpiskās Autokorelācijas Paplašinājums Laikā un Telpā	Global Moran's I
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1981–1992	1950
Autors	Cliff & Ord; extended by Anselin and others	Patrick Alfred Pierce Moran
Tips	Spatial autocorrelation statistic	Global spatial autocorrelation test / index
Pirmavots	Clifford, P., Richardson, S., & Hemon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 45(1), 123–134.	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Citi nosaukumi	STSA, spatiotemporal autocorrelation, space-time Moran's I, temporal spatial dependence	Moran's I, global spatial autocorrelation index, Moran index, GMI
Saistītās	5	6
Kopsavilkums	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.	Global Moran's I is the most widely used single-number summary of spatial autocorrelation across an entire study area. It compares the attribute value at each location with values at neighbouring locations using a spatial weights matrix, and returns a statistic ranging from −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering). A significance test determines whether the observed pattern is stronger than random chance.