Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Paneļu tīklā balstīta telpiskā analīze× | Paneļu ģeogrāfiski svērtā regresija (Panel GWR)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads | 2000s–2010s | 2000s–2010s |
| Autors≠ | Developed from LeSage & Pace spatial econometrics and Elhorst panel spatial frameworks | Fotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature |
| Tips≠ | Panel spatial regression | Local spatial regression with panel structure |
| Pirmavots≠ | LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Citi nosaukumi | panel spatial network analysis, longitudinal network spatial analysis, panel network spatial econometrics, PNBSA | Panel GWR, PGWR, spatiotemporal GWR, geographically weighted panel regression |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Panel Network-Based Spatial Analysis extends standard spatial econometric models to repeated-measures (panel) data by representing spatial dependence through network connectivity rather than simple geographic proximity. It captures how units connected in a network influence each other's outcomes over time, while controlling for unit-level and time-level fixed effects. | 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. |
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