方法对比

	反距离加权法 (IDW)	共克里金	地理加权回归 (GWR)	通用克里金 (带趋势的克里金)
领域	空间分析	空间分析	空间分析	空间分析
方法族	Regression model	Regression model	Regression model	Regression model
起源年份	1968	1963	2002	1969
提出者	Donald Shepard	Georges Matheron (geostatistics); multivariate extension	Fotheringham, Brunsdon & Charlton	Georges Matheron
类型	Deterministic spatial interpolation	Multivariate geostatistical interpolation	Local spatial regression	Geostatistical interpolation with spatial trend
开创性文献	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.	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.
别名	IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon	co-kriging, multivariate kriging, ortak kriging	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
相关	3	3	5	3
摘要	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.	Cokriging extends kriging to use one or more correlated secondary variables to improve prediction of a primary variable. When the variable of interest is sparsely sampled but a related, cheaper-to-measure variable is densely sampled, cokriging borrows strength from the secondary variable through their cross-correlation, yielding more accurate interpolations and prediction variances than kriging the primary variable alone.	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.