Confronta i metodi

	Regressiona Spaziale Locale	Modello di Errore Spaziale (SEM)
Campo	Analisi spaziale	Analisi spaziale
Famiglia	Regression model	Regression model
Anno di origine	1996	1988
Ideatore	Brunsdon, Fotheringham & Charlton	Anselin
Tipo	Spatially varying coefficient regression	Spatial regression (spatially autocorrelated errors)
Fonte seminale	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Alias	locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression	SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error)
Correlati	6	5
Sintesi	Local Spatial Regression fits a separate regression model at each location in a study area, allowing regression coefficients to vary continuously across space. Rather than forcing one global slope on all observations, it reveals where and how the relationship between predictors and an outcome changes geographically — producing a map of coefficients rather than a single number.	The Spatial Error Model, developed within Anselin's spatial econometrics framework (1988), is a regression model that assumes spatial dependence enters through the error term: the disturbances of neighbouring units are correlated. It is used when unobserved shared factors make the errors of nearby observations move together, and it is estimated by maximum likelihood or GMM rather than ordinary least squares.