Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Co-Kriging Global× | Autocorelația spațială× | |
|---|---|---|
| Domeniu | Analiză spațială | Analiză spațială |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1982 | 1950 |
| Autorul original≠ | Matheron (geostatistics framework); formalized for multivariate case by Myers (1982) | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| Tip≠ | Multivariate geostatistical interpolation | Spatial statistic / exploratory spatial data analysis |
| Sursa seminală≠ | Myers, D. E. (1982). Matrix formulation of co-kriging. Journal of the International Association for Mathematical Geology, 14(3), 249–257. DOI ↗ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Denumiri alternative | global cokriging, co-kriging, cokriging, multivariate kriging | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| Înrudite≠ | 4 | 5 |
| Rezumat≠ | Global Co-Kriging is a multivariate geostatistical interpolation method that estimates an unsampled primary variable by exploiting its spatial cross-correlation with one or more secondary variables. Unlike local (moving-window) approaches, it fits a single set of variogram and cross-variogram models to the entire study domain and solves one global cokriging system for each prediction location. | Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations. |
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