Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Moran's I wa Wakati-Nafasi× | Moran's I× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Kimaeneo | Uchanganuzi wa Kimaeneo |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1981 | 1950 |
| Mwanzilishi≠ | Cliff & Ord (extended to space-time domain) | Patrick A. P. Moran |
| Aina | Spatial autocorrelation statistic | Spatial autocorrelation statistic |
| Chanzo asilia≠ | Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion. ISBN: 978-0850860818 | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Majina mbadala | space-time autocorrelation index, ST Moran's I, spatiotemporal Moran's I, space-time I statistic | Moran's I statistic, global Moran's I, spatial autocorrelation index, Moran index |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | Space-Time Moran's I extends the classic Moran's I statistic into the spatiotemporal domain, measuring whether observations that are close in both space and time tend to be more similar than those that are distant. It detects clustering, dispersion, or randomness across a combined space-time weight matrix, making it a foundational tool in epidemiology, criminology, and environmental monitoring. | Moran's I is the standard global statistic for detecting spatial autocorrelation: whether nearby locations tend to share similar values. The index ranges from approximately −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering), allowing researchers to test whether a geographic pattern differs from complete spatial randomness with a single, interpretable number. |
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