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Kookriging×Regresi Berbobot Geografis (GWR)×
BidangAnalisis SpasialAnalisis Spasial
KeluargaRegression modelRegression model
Tahun asal19632002
PencetusGeorges Matheron (geostatistics); multivariate extensionFotheringham, Brunsdon & Charlton
TipeMultivariate geostatistical interpolationLocal spatial regression
Sumber perintisMatheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. DOI ↗Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Aliasco-kriging, multivariate kriging, ortak krigingGWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Terkait35
RingkasanCokriging 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.
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ScholarGateBandingkan metode: Cokriging · Geographically Weighted Regression. Diakses 2026-06-18 dari https://scholargate.app/id/compare