Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi wa Maeneo Moto ya Muda-Nafasi× | Uchambuzi wa Maeneo Moto (Getis-Ord Gi*)× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Kimaeneo | Uchanganuzi wa Kimaeneo |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1997–2015 | 1992 |
| Mwanzilishi≠ | Kulldorff (spatial scan statistic); operationalized for time-series bins by Esri (Emerging Hot Spot Analysis) | Arthur Getis and J. Keith Ord |
| Aina≠ | Spatiotemporal cluster detection | Local spatial statistic |
| Chanzo asilia≠ | Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26(6), 1481–1496. DOI ↗ | Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. DOI ↗ |
| Majina mbadala | emerging hot spot analysis, space-time cube hot spot, spatiotemporal hot spot detection, STHA | Getis-Ord Gi* statistic, spatial hot spot detection, cluster and outlier analysis, HSA |
| 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. | Hot Spot Analysis uses the Getis-Ord Gi* local spatial statistic to identify geographic locations where high or low attribute values cluster together to a degree that is statistically significant. Each feature is evaluated in relation to its neighbours, producing a z-score that flags genuine spatial hot spots and cold spots against a background of random variation. |
| ScholarGateSeti ya data ↗ |
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