Порівняння методів

	Локальна просторова регресія	Багатомасштабна географічно зважена регресія (MGWR)
Галузь	Просторовий аналіз	Просторовий аналіз
Родина	Regression model	Regression model
Рік появи	1996	2017
Автор методу	Brunsdon, Fotheringham & Charlton	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Тип	Spatially varying coefficient regression	Local spatial regression
Основоположне джерело	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
Інші назви	locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Пов'язані	6	5
Підсумок	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.	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.