Regression model
多尺度地理加权回归 (MGWR)
多尺度地理加权回归 (Multiscale Geographically Weighted Regression, MGWR) 由 Fotheringham、Yang 和 Kang 于 2017 年提出，是一种空间回归模型，它允许每个系数在空间上以各自的空间尺度变化。该模型通过为每个预测变量分配独立的带宽，从而推广了地理加权回归 (Geographically Weighted Regression, GWR)，使得一些关系可以局部作用，而另一些则几乎全局作用。
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Method map
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来源
- 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: 10.1080/24694452.2017.1352480 ↗
- Oshan, T. M., Li, Z., Kang, W., Wolf, L. J. & Fotheringham, A. S. (2019). mgwr: A Python Implementation of Multiscale Geographically Weighted Regression. Journal of Open Source Software, 4(42), 1670. link ↗
如何引用本页
ScholarGate. (2026, June 1). Multiscale Geographically Weighted Regression. ScholarGate. https://scholargate.app/zh/spatial-analysis/mgwr-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 地理加权回归 (GWR)空间分析↔ compare
- Getis-Ord Gi* 热点分析空间分析↔ compare
- 普通最小二乘法 (OLS) 回归计量经济学↔ compare
- 空间误差模型 (SEM)空间分析↔ compare
- 空间滞后模型（SAR / 空间自回归）空间分析↔ compare