مقایسهٔ روشها
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| رگرسیون فضایی-زمانی فضایی× | کرایجینگ فضا-زمان× | |
|---|---|---|
| حوزه | تحلیل فضایی | تحلیل فضایی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 1990s–2000s | 1999 |
| پدیدآور≠ | Anselin, LeSage, Pace and colleagues in spatial econometrics | Cressie & Huang; Kyriakidis & Journel |
| نوع≠ | Spatio-temporal regression model | Geostatistical interpolation |
| منبع بنیادین≠ | LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 | Cressie, N., & Huang, H.-C. (1999). Classes of nonseparable, spatio-temporal stationary covariance functions. Journal of the American Statistical Association, 94(448), 1330-1340. DOI ↗ |
| نامهای دیگر | spatio-temporal regression, spatial panel regression, space-time regression, ST spatial regression | spatiotemporal kriging, ST-kriging, space-time geostatistical interpolation, kriging in space-time |
| مرتبط≠ | 6 | 4 |
| خلاصه≠ | Space-Time Spatial Regression extends classical spatial regression to panel settings where georeferenced units are observed across multiple time periods. By embedding a spatial weights matrix into a panel regression framework, it simultaneously controls for spatial dependence among cross-sectional units and temporal dynamics, yielding unbiased and consistent estimates in spatio-temporal data. | Space-Time Kriging is a geostatistical interpolation method that predicts an unknown variable at any location and time by borrowing strength from nearby observations in both space and time simultaneously. It models the joint spatial-temporal covariance structure through a space-time variogram, then uses optimal linear weights to produce predictions with quantified uncertainty. |
| ScholarGateمجموعهداده ↗ |
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