Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mchoro wa Kanga wa Kijiografia (SDM)× | Usanifu wa Usajili wa Kawaida wa Kijiografia wa Kiwango-Nyingi (MGWR)× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Kimaeneo | Uchanganuzi wa Kimaeneo |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2009 | 2017 |
| Mwanzilishi≠ | LeSage & Pace | Fotheringham, Yang & Kang |
| Aina≠ | Spatial regression model | Spatially varying coefficient regression |
| Chanzo asilia≠ | LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. 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 ↗ |
| Majina mbadala | SDM, spatial mixed model, uzamsal durbin modeli | multiscale GWR, multi-scale geographically weighted regression, Çok Ölçekli Coğrafi Ağırlıklı Regresyon (MGWR) |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | The Spatial Durbin Model is a general spatial regression model that includes a spatial lag of both the dependent variable (ρWy) and the explanatory variables (WXθ). Introduced as the recommended starting point by LeSage and Pace (2009), it nests the spatial autoregressive (SAR) and spatial error (SEM) models as special cases. | Multiscale Geographically Weighted Regression, introduced by Fotheringham, Yang and Kang in 2017, is a spatial regression model that lets each coefficient vary across space at its own spatial scale. It generalises Geographically Weighted Regression by giving every predictor its own bandwidth, so some relationships can act locally while others act almost globally. |
| ScholarGateSeti ya data ↗ |
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