手法を比較

	局所地理加重回帰 (GWR)	Multiscale Geographically Weighted Regression (MGWR)
分野	空間分析	空間分析
系統	Regression model	Regression model
提唱年	1996	2017
提唱者	Brunsdon, Fotheringham & Charlton	A. Stewart Fotheringham, Wei Yang, and Wei Kang
種類	Spatially varying coefficient regression	Local spatial regression
原典	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.
別名	GWR, geographically weighted regression, local spatial regression, spatially varying coefficient model	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
関連	5	5
概要	Local Geographically Weighted Regression (GWR) estimates a separate regression model at each location in the study area, allowing every coefficient to vary spatially. By weighting nearby observations more heavily than distant ones, GWR reveals how predictor-outcome relationships shift across geographic space rather than forcing a single global estimate on heterogeneous data.	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.