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| الانحدار المكاني عبر الزمان والمكان× | كرِيجة الزمكان× | |
|---|---|---|
| المجال | التحليل المكاني | التحليل المكاني |
| العائلة | Regression model | Regression model |
| سنة النشأة≠ | 1990s–2000s | 1999 |
| صاحب الطريقة≠ | Anselin, LeSage, Pace and colleagues in spatial econometrics | Cressie & Huang; Kyriakidis & Journel |
| النوع≠ | Spatio-temporal regression model | Geostatistical interpolation |
| المصدر التأسيسي≠ | 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 ↗ |
| الأسماء البديلة | spatio-temporal regression, spatial panel regression, space-time regression, ST spatial regression | spatiotemporal kriging, ST-kriging, space-time geostatistical interpolation, kriging in space-time |
| ذات صلة≠ | 6 | 4 |
| الملخص≠ | 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. |
| ScholarGateمجموعة البيانات ↗ |
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