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| Panel Multiscale Geographically Weighted Regression (Panel MGWR)× | Regresi Berbobot Geografis (GWR)× | |
|---|---|---|
| Bidang | Analisis Spasial | Analisis Spasial |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2017-2020 | 2002 |
| Pencetus≠ | Fotheringham, Yang & Kang (MGWR base); panel extension developed in spatial econometrics literature | Fotheringham, Brunsdon & Charlton |
| Tipe≠ | Spatially varying coefficient panel regression | Local spatial regression |
| Sumber perintis≠ | 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., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Alias | Panel MGWR, MGWR panel data, multiscale GWR panel, panel spatially varying coefficient model | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Terkait | 5 | 5 |
| Ringkasan≠ | 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. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. |
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