Сравнение методов

	Локальная пространственная регрессия	Многомасштабная географически взвешенная регрессия (MGWR)
Область	Пространственный анализ	Пространственный анализ
Семейство	Regression model	Regression model
Год появления	1996	2017
Автор метода	Brunsdon, Fotheringham & Charlton	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Тип	Spatially varying coefficient regression	Local spatial regression
Основополагающий источник	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.
Другие названия	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
Связанные	6	5
Сводка	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.