Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Панельна багатомасштабна географічно зважена регресія (Panel MGWR)× | Багатомасштабна географічно зважена регресія (MGWR)× | |
|---|---|---|
| Галузь | Просторовий аналіз | Просторовий аналіз |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2017-2020 | 2017 |
| Автор методу≠ | Fotheringham, Yang & Kang (MGWR base); panel extension developed in spatial econometrics literature | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Тип≠ | Spatially varying coefficient panel regression | Local spatial regression |
| Основоположне джерело | 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 ↗ | 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 ↗ |
| Інші назви | Panel MGWR, MGWR panel data, multiscale GWR panel, panel spatially varying coefficient model | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Пов'язані | 5 | 5 |
| Підсумок≠ | Panel MGWR extends Multiscale Geographically Weighted Regression to repeated-observations (panel) data, allowing each predictor to operate at its own spatial bandwidth while controlling for unit-specific or time-specific fixed effects. It is used when both spatial heterogeneity and temporal structure matter simultaneously. | 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. |
| ScholarGateНабір даних ↗ |
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