Linganisha mbinu

	Co-kriging: Uingizaji wa Njia Mbalimbali za Kijiografia	Uchanganuzi wa Regresheni yenye Uzito wa Kijiografia wa Mizani Mingi (MGWR)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1965-1978	2017
Mwanzilishi	Matheron, G.; extended by Journel & Huijbregts	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Aina	Geostatistical interpolation	Local spatial regression
Chanzo asilia	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.
Majina mbadala	cokriging, co-regionalization kriging, multivariate kriging, CK	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Zinazohusiana	5	5
Muhtasari	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.