Salīdzināt metodes

	Lokālais telpiskais Urbina modelis	Lokālā telpiskā regresija
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	2002–2009	1996
Autors	LeSage & Pace (SDM foundation); local adaptation via Fotheringham et al. GWR framework	Brunsdon, Fotheringham & Charlton
Tips	Spatially varying regression model	Spatially varying coefficient regression
Pirmavots	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Citi nosaukumi	local SDM, geographically weighted Spatial Durbin Model, GW-SDM, spatially varying Durbin model	locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression
Saistītās	5	6
Kopsavilkums	The Local Spatial Durbin Model (Local SDM) extends the global Spatial Durbin Model by allowing regression coefficients to vary across geographic space. It combines the SDM's ability to capture both spatial lag of the dependent variable and spatial lags of covariates with a geographically weighted estimation framework, producing location-specific direct and indirect spillover effects.	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.