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चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| मल्टीस्केल ज्योग्राफिकली वेटेड रिग्रेशन (MGWR)× | स्थानिक लैग मॉडल (SAR / स्थानिक ऑटोरिग्रेसिव)× | |
|---|---|---|
| क्षेत्र | स्थानिक विश्लेषण | स्थानिक विश्लेषण |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 2017 | 1988 |
| प्रवर्तक≠ | A. Stewart Fotheringham, Wei Yang, and Wei Kang | Anselin (textbook formalisation); LeSage & Pace |
| प्रकार≠ | Local spatial regression | Spatial autoregressive regression |
| मौलिक स्रोत≠ | 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 ↗ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ |
| उपनाम | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR | SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag) |
| संबंधित | 5 | 5 |
| सारांश≠ | 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. | The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts. |
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