Сравнение на методи

	Метод на обратно пропорционално претегляне на разстоянието (IDW)	Кокригинг	Универсално кригиране (Кригиране с тренд)
Област	Пространствен анализ	Пространствен анализ	Пространствен анализ
Семейство	Regression model	Regression model	Regression model
Година на възникване	1968	1963	1969
Създател	Donald Shepard	Georges Matheron (geostatistics); multivariate extension	Georges Matheron
Тип	Deterministic spatial interpolation	Multivariate geostatistical interpolation	Geostatistical interpolation with spatial trend
Основополагащ източник	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.	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
Други названия	IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon	co-kriging, multivariate kriging, ortak kriging	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
Свързани	3	3	3
Резюме	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.	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.