Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Globālais telpiskais Durbina modelis (SDM)× | Daudzskalu ģeogrāfiski svērtā regresija (MGWR)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2009 | 2017 |
| Autors≠ | Durbin (1960); adapted to spatial context by LeSage & Pace (2009) | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Tips≠ | Spatial regression model | Local spatial regression |
| Pirmavots≠ | LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 | 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 | SDM, Spatial Durbin Model, global SDM, spatially lagged X model with spatial lag | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Global Spatial Durbin Model extends the spatial lag model by including not only a spatially lagged dependent variable but also spatially lagged independent variables (WX). A single set of global coefficients applies uniformly across all locations, making it suitable for estimating average spillover effects when spatial dependence is pervasive throughout the study region. | 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. |
| ScholarGateDatu kopa ↗ |
|
|