Linganisha mbinu

	Usuli wa Kawaida wa Kijiografia (GWR)	Njia ya Uzito wa Umbali wa Kinyume (IDW)	Ukridingi wa Ulimwengu (Ukridingi wenye Mwenendo)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	2002	1968	1969
Mwanzilishi	Fotheringham, Brunsdon & Charlton	Donald Shepard	Georges Matheron
Aina	Local spatial regression	Deterministic spatial interpolation	Geostatistical interpolation with spatial trend
Chanzo asilia	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168	Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524.	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
Majina mbadala	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)	IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
Zinazohusiana	5	3	3
Muhtasari	Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.	Inverse distance weighting is a simple, deterministic method for estimating values at unsampled locations by taking a weighted average of nearby measured points, where closer points carry more weight. Introduced by Donald Shepard in 1968, it embodies the first law of geography — near things are more related than distant things — and is one of the most widely used interpolation methods in GIS for mapping continuous fields such as rainfall, elevation, or pollution from scattered samples.	Universal kriging generalizes ordinary kriging to data whose mean varies systematically across space — a spatial trend or 'drift'. It models the mean as a function of the coordinates (or covariates) and krigs the residuals, so it can interpolate variables that drift in a preferred direction, such as temperature falling with latitude or a pollutant gradient, while still returning prediction variances.