Сравнение методов

	Локальная модель пространственного лага	Многомасштабная географически взвешенная регрессия (MGWR)
Область	Пространственный анализ	Пространственный анализ
Семейство	Regression model	Regression model
Год появления	1988 (global); 2000s (local extensions)	2017
Автор метода	Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002)	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Тип	Spatially varying regression model	Local spatial regression
Основополагающий источник	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737215	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
Другие названия	local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Связанные	5	5
Сводка	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.	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.