Salīdzināt metodes

	Lokālā telpiskā regresija	Telpiskais kļūdu modelis (SEM)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1996	1988
Autors	Brunsdon, Fotheringham & Charlton	Anselin
Tips	Spatially varying coefficient regression	Spatial regression (spatially autocorrelated errors)
Pirmavots	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.
Citi nosaukumi	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)
Saistītās	6	5
Kopsavilkums	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.