Vertaile menetelmiä

	Käänteinen etäisyyspainotus (IDW)	Paikallisesti painotettu regressio (GWR)	Universaali Kriging (Kriging trendillä)
Tieteenala	Spatiaalianalyysi	Spatiaalianalyysi	Spatiaalianalyysi
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	1968	2002	1969
Kehittäjä	Donald Shepard	Fotheringham, Brunsdon & Charlton	Georges Matheron
Tyyppi	Deterministic spatial interpolation	Local spatial regression	Geostatistical interpolation with spatial trend
Alkuperäislähde	Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
Rinnakkaisnimet	IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
Liittyvät	3	5	3
Tiivistelmä	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.	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.	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.