Сравнение методов

	Bayesian Multiscale Geographically Weighted Regression	Локальная пространственная регрессия
Область	Пространственный анализ	Пространственный анализ
Семейство	Regression model	Regression model
Год появления	2017-2020	1996
Автор метода	Fotheringham, Yang & Kang (MGWR); Bayesian extension by Li and co-authors	Brunsdon, Fotheringham & Charlton
Тип	Spatially varying coefficient regression	Spatially varying coefficient regression
Основополагающий источник	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Другие названия	Bayesian MGWR, B-MGWR, Bayesian multiscale GWR, Bayesian spatially varying coefficient model	locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression
Связанные	6	6
Сводка	Bayesian Multiscale Geographically Weighted Regression (Bayesian MGWR) extends the MGWR framework by placing Bayesian priors on each spatially varying coefficient. Each predictor is allowed its own bandwidth — its own geographic scale of influence — while Bayesian inference replaces classical back-fitting with posterior sampling, yielding full uncertainty quantification for every local coefficient surface.	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.