Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Мультимасштабный геопространственно взвешенный регрессионный анализ (MGWR)× | Анализ горячих точек Getis-Ord Gi*× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2017 | 1992 |
| Автор метода≠ | Fotheringham, Yang & Kang | Arthur Getis and J. Keith Ord |
| Тип≠ | Spatially varying coefficient regression | Local spatial statistic |
| Основополагающий источник≠ | Fotheringham, A. S., Yang, W. & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265. DOI ↗ | Getis, A. & Ord, J.K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24(3), 189–206. DOI ↗ |
| Другие названия≠ | multiscale GWR, multi-scale geographically weighted regression, Çok Ölçekli Coğrafi Ağırlıklı Regresyon (MGWR) | hot spot analysis, cold spot analysis, Gi* statistic, local Gi statistic |
| Связанные≠ | 5 | 4 |
| Сводка≠ | Multiscale Geographically Weighted Regression, introduced by Fotheringham, Yang and Kang in 2017, is a spatial regression model that lets each coefficient vary across space at its own spatial scale. It generalises Geographically Weighted Regression by giving every predictor its own bandwidth, so some relationships can act locally while others act almost globally. | Getis-Ord Gi* is a local spatial statistic, introduced by Getis and Ord in 1992 and refined in 1995, that compares the value at each location and its neighbours against the global mean to identify statistically significant clusters of high values (hot spots) and low values (cold spots). |
| ScholarGateНабор данных ↗ |
|
|