手法を比較

	共同クルギング：多変量地球統計学的手法による補間	Multiscale Geographically Weighted Regression (MGWR)
分野	空間分析	空間分析
系統	Regression model	Regression model
提唱年	1965-1978	2017
提唱者	Matheron, G.; extended by Journel & Huijbregts	A. Stewart Fotheringham, Wei Yang, and Wei Kang
種類	Geostatistical interpolation	Local spatial regression
原典	Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561	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	cokriging, co-regionalization kriging, multivariate kriging, CK	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
関連	5	5
概要	Co-kriging is a geostatistical interpolation technique that predicts the spatial distribution of a primary variable by leveraging its spatial cross-correlation with one or more secondary (co-) variables. It extends ordinary kriging to multivariate settings, yielding more accurate predictions when the secondary variable is more densely sampled or spatially correlated with the primary variable of interest.	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.