Сравнение методов

	Глобальная пространственная модель ошибок (SEM)	Пространственная автокорреляция
Область	Пространственный анализ	Пространственный анализ
Семейство	Regression model	Regression model
Год появления	1988	1950
Автор метода	Luc Anselin	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Тип	Spatial regression model	Spatial statistic / exploratory spatial data analysis
Основополагающий источник	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.
Другие названия	SEM, spatial error model, spatial error regression, global SEM	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Связанные	5	5
Сводка	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.