Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi wa Maeneo Moto ya Muda-Nafasi× | Moran's I wa Wakati-Nafasi× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Kimaeneo | Uchanganuzi wa Kimaeneo |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1997–2015 | 1981 |
| Mwanzilishi≠ | Kulldorff (spatial scan statistic); operationalized for time-series bins by Esri (Emerging Hot Spot Analysis) | Cliff & Ord (extended to space-time domain) |
| Aina≠ | Spatiotemporal cluster detection | Spatial autocorrelation statistic |
| Chanzo asilia≠ | Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26(6), 1481–1496. DOI ↗ | Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion. ISBN: 978-0850860818 |
| Majina mbadala | emerging hot spot analysis, space-time cube hot spot, spatiotemporal hot spot detection, STHA | space-time autocorrelation index, ST Moran's I, spatiotemporal Moran's I, space-time I statistic |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | Space-Time Hot Spot Analysis extends the classic Getis-Ord Gi* statistic across repeated time slices organised in a space-time cube. By testing each location-time bin for statistically significant clustering of high or low values, then examining the sequence of results over time, it identifies whether clusters are new, intensifying, persistent, sporadic, or diminishing — giving analysts a dynamic picture of how hot and cold spots evolve. | 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. |
| ScholarGateSeti ya data ↗ |
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