Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Tīklā balstīta telpiskā analīze× | Karstā punkta analīze (Getis-Ord Gi*)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1990s–2000s | 1992 |
| Autors≠ | Atsuyuki Okabe and colleagues | Arthur Getis and J. Keith Ord |
| Tips≠ | Spatial network model | Local spatial statistic |
| Pirmavots≠ | Okabe, A., Satoh, T., Furuta, T., Sugihara, K., & Okano, K. (2006). Generalized network Voronoi diagrams: Concepts, computational methods, and applications. International Journal of Geographical Information Science, 22(9), 965–994. DOI ↗ | Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. DOI ↗ |
| Citi nosaukumi | network spatial analysis, network-constrained spatial analysis, spatial network analysis, NBSA | Getis-Ord Gi* statistic, spatial hot spot detection, cluster and outlier analysis, HSA |
| Saistītās≠ | 3 | 5 |
| Kopsavilkums≠ | Network-based spatial analysis (NBSA) analyzes the distribution and interaction of spatial phenomena constrained to a network structure — such as roads, railways, or rivers — using network distance rather than straight-line (Euclidean) distance. It is the appropriate framework whenever movement, proximity, or risk is governed by the underlying network topology rather than open space. | 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. |
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