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| マルチスケールGetis-Ord Gi* ホットスポット分析× | Multiscale Geographically Weighted Regression (MGWR)× | |
|---|---|---|
| 分野 | 空間分析 | 空間分析 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1995 (Gi* basis); multiscale application 2000s onward | 2017 |
| 提唱者≠ | Ord & Getis (1995); multiscale extension developed in applied spatial analysis practice | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| 種類≠ | Local spatial statistic (multiscale) | Local spatial regression |
| 原典≠ | Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286-306. DOI ↗ | 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 ↗ |
| 別名 | multi-distance Gi*, multiscale hot spot analysis, multi-bandwidth Getis-Ord, scale-varying Gi* | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| 関連 | 5 | 5 |
| 概要≠ | Multiscale Getis-Ord Gi* extends the classic local hot spot statistic by computing Gi* z-scores across a range of spatial distance bands or neighborhood sizes. This reveals whether clusters of high or low values are scale-dependent — appearing only at fine local scales, only at broad regional scales, or persistently across all scales — providing richer spatial intelligence than a single-bandwidth analysis. | Multiscale Geographically Weighted Regression (MGWR) is a local spatial regression framework that relaxes the single-bandwidth constraint of standard GWR by allowing each predictor to operate at its own spatial scale. Each coefficient surface is calibrated with its own bandwidth, enabling the model to distinguish drivers that vary slowly across space from those that vary sharply. |
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