विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| स्थानीय स्थानिक लैग मॉडल× | मल्टीस्केल ज्योग्राफिकली वेटेड रिग्रेशन (MGWR)× | |
|---|---|---|
| क्षेत्र | स्थानिक विश्लेषण | स्थानिक विश्लेषण |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1988 (global); 2000s (local extensions) | 2017 |
| प्रवर्तक≠ | Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002) | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| प्रकार≠ | Spatially varying regression model | Local spatial regression |
| मौलिक स्रोत≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737215 | 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 ↗ |
| उपनाम | local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| संबंधित | 5 | 5 |
| सारांश≠ | The Local Spatial Lag Model extends the classical spatial lag model by allowing both the spatial autocorrelation parameter and the regression coefficients to vary across geographic locations. Instead of one global estimate of how neighboring outcomes influence each observation, the model fits location-specific parameters using kernel-weighted local estimation, revealing spatial heterogeneity in spatial dependence. | 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. |
| ScholarGateडेटासेट ↗ |
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