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| Regresi Berwajaran Geografi Panel (Panel GWR)× | Regresi Berwajaran Geografi (GWR) Tempatan× | |
|---|---|---|
| Bidang | Analisis Reruang | Analisis Reruang |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2000s–2010s | 1996 |
| Pengasas≠ | Fotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature | Brunsdon, Fotheringham & Charlton |
| Jenis≠ | Local spatial regression with panel structure | Spatially varying coefficient regression |
| Sumber perintis | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Alias | Panel GWR, PGWR, spatiotemporal GWR, geographically weighted panel regression | GWR, geographically weighted regression, local spatial regression, spatially varying coefficient model |
| Berkaitan≠ | 4 | 5 |
| Ringkasan≠ | Panel Geographically Weighted Regression (Panel GWR) extends the standard GWR framework to panel data, allowing regression coefficients to vary both across geographic locations and over time. It captures spatially non-stationary relationships in longitudinal or repeated-measures spatial datasets, combining local spatial estimation with panel-data controls for unit-specific heterogeneity. | Local Geographically Weighted Regression (GWR) estimates a separate regression model at each location in the study area, allowing every coefficient to vary spatially. By weighting nearby observations more heavily than distant ones, GWR reveals how predictor-outcome relationships shift across geographic space rather than forcing a single global estimate on heterogeneous data. |
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