Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Панельная многомасштабная географически взвешенная регрессия (Panel MGWR)× | Многомасштабная географически взвешенная регрессия (MGWR)× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2017-2020 | 2017 |
| Автор метода≠ | 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 |
| Основополагающий источник | 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 ↗ |
| Другие названия | Panel MGWR, MGWR panel data, multiscale GWR panel, panel spatially varying coefficient model | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Связанные | 5 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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