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| كرِيجة الزمكان× | الارتباط التلقائي المكاني الزماني-المكاني× | |
|---|---|---|
| المجال | التحليل المكاني | التحليل المكاني |
| العائلة | Regression model | Regression model |
| سنة النشأة≠ | 1999 | 1981–1992 |
| صاحب الطريقة≠ | Cressie & Huang; Kyriakidis & Journel | Cliff & Ord; extended by Anselin and others |
| النوع≠ | Geostatistical interpolation | Spatial autocorrelation statistic |
| المصدر التأسيسي≠ | 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 ↗ | Clifford, P., Richardson, S., & Hemon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 45(1), 123–134. DOI ↗ |
| الأسماء البديلة | spatiotemporal kriging, ST-kriging, space-time geostatistical interpolation, kriging in space-time | STSA, spatiotemporal autocorrelation, space-time Moran's I, temporal spatial dependence |
| ذات صلة≠ | 4 | 5 |
| الملخص≠ | 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. | 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. |
| ScholarGateمجموعة البيانات ↗ |
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