Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Paneļu ģeogrāfiski svērtā regresija (Panel GWR)× | Panel Spatial Error Model× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2000s–2010s | 1988 / 2003 |
| Autors≠ | Fotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature | Anselin (1988); extended to panels by Elhorst (2003, 2014) |
| Tips≠ | Local spatial regression with panel structure | Spatial econometric panel model |
| Pirmavots≠ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408 |
| Citi nosaukumi | Panel GWR, PGWR, spatiotemporal GWR, geographically weighted panel regression | panel SEM, spatial error panel model, panel spatial autocorrelation error model, SEM panel |
| 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. | The Panel Spatial Error Model (panel SEM) extends the classical spatial error model to panel data, allowing spatial dependence to enter through the error term across cross-sectional units over multiple time periods. It accounts for spatially correlated omitted variables without imposing a substantive spatial spillover in the outcome itself. |
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