Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Paikallinen ydinestimaattitiheysestimaatti× | Verkkopohjainen tilastollinen analyysi× | |
|---|---|---|
| Tieteenala | Spatiaalianalyysi | Spatiaalianalyysi |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1985-1986 | 1990s–2000s |
| Kehittäjä≠ | Silverman, B. W.; Diggle, P. J. | Atsuyuki Okabe and colleagues |
| Tyyppi≠ | Non-parametric density estimator | Spatial network model |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet | Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation | network spatial analysis, network-constrained spatial analysis, spatial network analysis, NBSA |
| Liittyvät≠ | 5 | 3 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
|
|