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Environmental drivers of urban shrinkage in the Tokyo metropolitan area: A machine learning analysis of nonlinearity and spatial heterogeneity
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DOI：10.1016/j.habitatint.2026.103795.png)
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Abstract
En 中文
• High-resolution identification of urban shrinkage in the Tokyo Metropolitan Area. • Ten drivers of urban shrinkage identified from a 24-factor environmental framework. • Nonlinear and spatially heterogeneous effects revealed by machine learning methods. • West–east zonal belt pattern of shrinkage dominated by four environmental factors.
Keywords:
Urban shrinkage
Metropolitan areas
Nighttime light data
Random forest
Geographic weighted random forest
Spatial pattern
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