विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| ऑर्डिनरी क्रिगिंग× | को-क्रीगिंग: बहुभिन्नरूपी भूसांख्यिकीय अंतर्वेशन× | |
|---|---|---|
| क्षेत्र | स्थानिक विश्लेषण | स्थानिक विश्लेषण |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1963 | 1965-1978 |
| प्रवर्तक≠ | Georges Matheron (formalising D.G. Krige's empirical work) | Matheron, G.; extended by Journel & Huijbregts |
| प्रकार | Geostatistical interpolation | Geostatistical interpolation |
| मौलिक स्रोत≠ | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ | Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561 |
| उपनाम | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor | cokriging, co-regionalization kriging, multivariate kriging, CK |
| संबंधित≠ | 4 | 5 |
| सारांश≠ | Ordinary Kriging (OK) is the standard geostatistical method for interpolating a continuous spatial variable at unsampled locations. It derives optimal, unbiased weights from the spatial covariance structure of the data, making it the Best Linear Unbiased Predictor (BLUP) under stationarity assumptions. Unlike simpler distance-based methods, it also provides a prediction uncertainty (kriging variance) at every interpolated point. | Co-kriging is a geostatistical interpolation technique that predicts the spatial distribution of a primary variable by leveraging its spatial cross-correlation with one or more secondary (co-) variables. It extends ordinary kriging to multivariate settings, yielding more accurate predictions when the secondary variable is more densely sampled or spatially correlated with the primary variable of interest. |
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