Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Kohalik tuumtiheduse hindamine× | Võrgupõhine ruumianalüüs× | |
|---|---|---|
| Valdkond | Ruumianalüüs | Ruumianalüüs |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 1985-1986 | 1990s–2000s |
| Looja≠ | Silverman, B. W.; Diggle, P. J. | Atsuyuki Okabe and colleagues |
| Tüüp≠ | Non-parametric density estimator | Spatial network model |
| Algallikas≠ | Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. ISBN: 978-0412246203 | 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 ↗ |
| Rööpnimetused | Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation | network spatial analysis, network-constrained spatial analysis, spatial network analysis, NBSA |
| Seotud≠ | 5 | 3 |
| Kokkuvõte≠ | Local Kernel Density Estimation (Local KDE) is a non-parametric spatial method that estimates the density of point events at each location by applying a kernel function with a spatially adaptive bandwidth. Unlike global KDE, which uses a fixed bandwidth across the entire study area, Local KDE adjusts the smoothing window according to local data density, capturing fine-scale clustering where events are sparse or concentrated. | 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. |
| ScholarGateAndmestik ↗ |
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