방법 비교

	보편 크리깅 (추세가 있는 크리깅)	코크리깅	역거리 가중치법 (IDW)
분야	공간분석	공간분석	공간분석
계열	Regression model	Regression model	Regression model
기원 연도	1969	1963	1968
창시자	Georges Matheron	Georges Matheron (geostatistics); multivariate extension	Donald Shepard
유형	Geostatistical interpolation with spatial trend	Multivariate geostatistical interpolation	Deterministic spatial interpolation
원전	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.	Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524.
별칭	kriging with a trend, kriging with drift, trend kriging, evrensel kriging	co-kriging, multivariate kriging, ortak kriging	IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon
관련	3	3	3
요약	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.	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.	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.