Comparar métodos

	Regresión Espacial Local	Regresión Geográficamente Ponderada Multiescala (MGWR)
Campo	Análisis espacial	Análisis espacial
Familia	Regression model	Regression model
Año de origen	1996	2017
Autor original	Brunsdon, Fotheringham & Charlton	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Tipo	Spatially varying coefficient regression	Local spatial regression
Fuente seminal	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.
Alias	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
Relacionados	6	5
Resumen	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.