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| Локално обикновено кригиране× | Многомащабна географски претеглена регресия (MGWR)× | |
|---|---|---|
| Област | Пространствен анализ | Пространствен анализ |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1970s–1990s | 2017 |
| Създател≠ | Journel & Huijbregts; developed further by Goovaerts and Chiles & Delfiner | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Тип≠ | Geostatistical interpolation (local/moving-window variant) | Local spatial regression |
| Основополагащ източник≠ | Chiles, J.-P., & Delfiner, P. (1999). Geostatistics: Modeling Spatial Uncertainty. Wiley. ISBN: 978-0471083153 | 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 ↗ |
| Други названия | moving window kriging, local kriging, neighborhood kriging, LOK | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Свързани | 5 | 5 |
| Резюме≠ | Local Ordinary Kriging (LOK) is a geostatistical interpolation method that estimates values at unsampled locations using only a spatially defined moving neighborhood of nearby observations. By restricting each prediction to a local data window rather than the full dataset, LOK accommodates spatial non-stationarity, reduces computational cost, and often yields more accurate local predictions than global ordinary kriging. | 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 от данни ↗ |
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