Sammenlign metoder

	Global Spatial Error Model (SEM)	Rumlig Autokorrelation
Fagområde	Rumlig analyse	Rumlig analyse
Familie	Regression model	Regression model
Oprindelsesår	1988	1950
Ophavsperson	Luc Anselin	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Type	Spatial regression model	Spatial statistic / exploratory spatial data analysis
Oprindelig kilde	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737322	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Aliasser	SEM, spatial error model, spatial error regression, global SEM	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Relaterede	5	5
Resumé	The Global Spatial Error Model (SEM) is a spatial regression technique that accounts for spatially autocorrelated error terms using a single, globally constant spatial parameter. It separates genuine predictor effects from spatial nuisance dependence in the residuals, yielding unbiased and efficient coefficient estimates when spatial error correlation is present across all observations.	Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations.