Sammenlign metoder

	Lokal rumlig regression	Multiskal Geografisk Vægtet Regression (MGWR)
Fagområde	Rumlig analyse	Rumlig analyse
Familie	Regression model	Regression model
Oprindelsesår	1996	2017
Ophavsperson	Brunsdon, Fotheringham & Charlton	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Type	Spatially varying coefficient regression	Local spatial regression
Oprindelig kilde	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.
Aliasser	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
Relaterede	6	5
Resumé	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.