Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beijesa ģeogrāfiski svērta regresija (BGWR)× | Daudzskalu ģeogrāfiski svērtā regresija (MGWR)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2007 | 2017 |
| Autors≠ | Wheeler & Calder (2007); Finley (2011) | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Tips≠ | Bayesian spatially varying coefficient regression | Local spatial regression |
| Pirmavots≠ | Finley, A. O. (2011). Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence. Methods in Ecology and Evolution, 2(2), 143-154. DOI ↗ | 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 | BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Bayesian Geographically Weighted Regression combines the spatially varying coefficient framework of GWR with Bayesian inference, placing Gaussian process priors on the locally varying regression coefficients. This yields full posterior distributions over each coefficient at every location, providing principled uncertainty quantification rather than only point estimates. | 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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