Salīdzināt metodes

	Lokālais telpiskais Urbina modelis	Daudzskalu ģeogrāfiski svērtā regresija (MGWR)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	2002–2009	2017
Autors	LeSage & Pace (SDM foundation); local adaptation via Fotheringham et al. GWR framework	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Tips	Spatially varying regression model	Local spatial regression
Pirmavots	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
Citi nosaukumi	local SDM, geographically weighted Spatial Durbin Model, GW-SDM, spatially varying Durbin model	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Saistītās	5	5
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.	Multiscale Geographically Weighted Regression (MGWR) is a local spatial regression framework that relaxes the single-bandwidth constraint of standard GWR by allowing each predictor to operate at its own spatial scale. Each coefficient surface is calibrated with its own bandwidth, enabling the model to distinguish drivers that vary slowly across space from those that vary sharply.