Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Panel Multiscale Geographically Weighted Regression× | Uchanganuzi wa Regresheni yenye Uzito wa Kijiografia wa Mizani Mingi (MGWR)× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Kimaeneo | Uchanganuzi wa Kimaeneo |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2017-2020 | 2017 |
| Mwanzilishi≠ | Fotheringham, Yang & Kang (MGWR base); panel extension developed in spatial econometrics literature | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Aina≠ | Spatially varying coefficient panel regression | Local spatial regression |
| Chanzo asilia | 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 ↗ |
| Majina mbadala | Panel MGWR, MGWR panel data, multiscale GWR panel, panel spatially varying coefficient model | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | 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. |
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