Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ горячих точек (Getis-Ord Gi*)× | Регрессия с географически взвешенными коэффициентами (GWR)× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1992 | 2002 |
| Автор метода≠ | Arthur Getis and J. Keith Ord | Fotheringham, Brunsdon & Charlton |
| Тип≠ | Local spatial statistic | Local spatial regression |
| Основополагающий источник≠ | Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. DOI ↗ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Другие названия | Getis-Ord Gi* statistic, spatial hot spot detection, cluster and outlier analysis, HSA | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. |
| ScholarGateНабор данных ↗ |
|
|