Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Telpiskās asociācijas vietējie indikatori (ST-LISA)× | Telpas-laika Morana I indekss× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1995 (LISA); space-time extensions developed 2000s–2010s | 1981 |
| Autors≠ | Extension of Anselin (1995) LISA framework to the space-time domain | Cliff & Ord (extended to space-time domain) |
| Tips≠ | Local spatial statistic (space-time) | Spatial autocorrelation statistic |
| Pirmavots≠ | Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ | Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion. ISBN: 978-0850860818 |
| Citi nosaukumi | ST-LISA, space-time LISA, spatiotemporal local indicators of spatial association, STLISA | space-time autocorrelation index, ST Moran's I, spatiotemporal Moran's I, space-time I statistic |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Space-Time Local Indicators of Spatial Association (ST-LISA) extend the classic LISA framework of Anselin (1995) into the temporal dimension, identifying locations that exhibit statistically significant spatial clustering or spatial outlier behavior consistently or intermittently across multiple time periods. They decompose global space-time autocorrelation into local contributions, revealing where and when spatial clusters emerge, persist, or dissolve. | 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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