Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Кокригинг: Многомерная геостатистическая интерполяция× | Многомасштабная географически взвешенная регрессия (MGWR)× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1965-1978 | 2017 |
| Автор метода≠ | Matheron, G.; extended by Journel & Huijbregts | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Тип≠ | Geostatistical interpolation | Local spatial regression |
| Основополагающий источник≠ | Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561 | Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. DOI ↗ |
| Другие названия | cokriging, co-regionalization kriging, multivariate kriging, CK | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | Multiscale Geographically Weighted Regression (MGWR) is a local spatial regression framework that relaxes the single-bandwidth constraint of standard GWR by allowing each predictor to operate at its own spatial scale. Each coefficient surface is calibrated with its own bandwidth, enabling the model to distinguish drivers that vary slowly across space from those that vary sharply. |
| ScholarGateНабор данных ↗ |
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