Salīdzināt metodes

	Lokālā telpiskā regresija	Lokālais telpiskās nobīdes modelis
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1996	1988 (global); 2000s (local extensions)
Autors	Brunsdon, Fotheringham & Charlton	Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002)
Tips	Spatially varying coefficient regression	Spatially varying regression model
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 Publishers. ISBN: 978-9024737215
Citi nosaukumi	locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression	local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model
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 Local Spatial Lag Model extends the classical spatial lag model by allowing both the spatial autocorrelation parameter and the regression coefficients to vary across geographic locations. Instead of one global estimate of how neighboring outcomes influence each observation, the model fits location-specific parameters using kernel-weighted local estimation, revealing spatial heterogeneity in spatial dependence.