مقایسهٔ روشها
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| خودهمبستگی فضایی-زمانی× | رگرسیون وزنی جغرافیایی (GWR)× | |
|---|---|---|
| حوزه | تحلیل فضایی | تحلیل فضایی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 1981–1992 | 2002 |
| پدیدآور≠ | Cliff & Ord; extended by Anselin and others | Fotheringham, Brunsdon & Charlton |
| نوع≠ | Spatial autocorrelation statistic | Local spatial regression |
| منبع بنیادین≠ | Clifford, P., Richardson, S., & Hemon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 45(1), 123–134. DOI ↗ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| نامهای دیگر | STSA, spatiotemporal autocorrelation, space-time Moran's I, temporal spatial dependence | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| مرتبط | 5 | 5 |
| خلاصه≠ | Space-Time Spatial Autocorrelation extends classic spatial autocorrelation measures — most notably Moran's I — to data that vary across both geographic units and time periods. It detects whether nearby locations that are also temporally close tend to share similar attribute values, revealing clusters, trends, or anomalies that purely spatial or purely temporal analyses would miss. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. |
| ScholarGateمجموعهداده ↗ |
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