विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| पैनल मल्टीस्केल ज्योग्राफिकली वेटेड रिग्रेशन (पैनल MGWR)× | मल्टीस्केल ज्योग्राफिकली वेटेड रिग्रेशन (MGWR)× | |
|---|---|---|
| क्षेत्र | स्थानिक विश्लेषण | स्थानिक विश्लेषण |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 2017-2020 | 2017 |
| प्रवर्तक≠ | Fotheringham, Yang & Kang (MGWR base); panel extension developed in spatial econometrics literature | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| प्रकार≠ | Spatially varying coefficient panel regression | Local spatial regression |
| मौलिक स्रोत | 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 ↗ |
| उपनाम | Panel MGWR, MGWR panel data, multiscale GWR panel, panel spatially varying coefficient model | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| संबंधित | 5 | 5 |
| सारांश≠ | 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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