Linganisha mbinu

	Utaalamu wa Uingizaji wa Nafasi wa Kriging	Usanifu wa Usajili wa Kawaida wa Kijiografia wa Kiwango-Nyingi (MGWR)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1963	2017
Mwanzilishi	Georges Matheron (formalised geostatistics)	Fotheringham, Yang & Kang
Aina	Geostatistical spatial interpolation	Spatially varying coefficient regression
Chanzo asilia	Matheron, G. (1963). Principles of Geostatistics. Economic Geology, 58(8), 1246–1266.	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	geostatistical interpolation, Gaussian process regression (geostatistics), ordinary kriging, Kriging (Mekânsal Enterpolasyon)	multiscale GWR, multi-scale geographically weighted regression, Çok Ölçekli Coğrafi Ağırlıklı Regresyon (MGWR)
Zinazohusiana	5	5
Muhtasari	Kriging is a geostatistical method that predicts the value of a continuous variable at unmeasured locations from nearby measurements, using the spatial correlation structure captured by a variogram. Formalised by Georges Matheron in 1963, it is the best linear unbiased predictor (BLUP) for spatial data and comes in Ordinary, Universal, and Co-Kriging forms.	Multiscale Geographically Weighted Regression, introduced by Fotheringham, Yang and Kang in 2017, is a spatial regression model that lets each coefficient vary across space at its own spatial scale. It generalises Geographically Weighted Regression by giving every predictor its own bandwidth, so some relationships can act locally while others act almost globally.