Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Telpiskās un laikiskās karstās vietas analīze× | Telpas-laika Morana I indekss× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1997–2015 | 1981 |
| Autors≠ | Kulldorff (spatial scan statistic); operationalized for time-series bins by Esri (Emerging Hot Spot Analysis) | Cliff & Ord (extended to space-time domain) |
| Tips≠ | Spatiotemporal cluster detection | Spatial autocorrelation statistic |
| Pirmavots≠ | 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 |
| Citi nosaukumi | 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 |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. |
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