Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Paneļu ģeogrāfiski svērtā regresija (Panel GWR)× | Daudzskalu ģeogrāfiski svērtā regresija (MGWR)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2000s–2010s | 2017 |
| Autors≠ | Fotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Tips≠ | Local spatial regression with panel structure | Local spatial regression |
| Pirmavots≠ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | 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 ↗ |
| Citi nosaukumi | Panel GWR, PGWR, spatiotemporal GWR, geographically weighted panel regression | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | 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. | 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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