방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 코크리깅: 다변량 지공간 보간법× | 공간 자기상관× | |
|---|---|---|
| 분야 | 공간분석 | 공간분석 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1965-1978 | 1950 |
| 창시자≠ | Matheron, G.; extended by Journel & Huijbregts | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| 유형≠ | Geostatistical interpolation | Spatial statistic / exploratory spatial data analysis |
| 원전≠ | Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561 | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| 별칭 | cokriging, co-regionalization kriging, multivariate kriging, CK | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| 관련 | 5 | 5 |
| 요약≠ | 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. | 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. |
| ScholarGate데이터셋 ↗ |
|
|