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<p>Amid ongoing climate change, the increasing frequency of extreme heat events has become a major concern, particularly in urban areas where the urban heat island effect intensifies thermal risks. Previous studies have largely focused on individual cities, with limited attention to their relationship with landscape patterns. This study investigated the nonlinear relationships between landscape indicators and urban heat risk using machine learning, based on multi-source data from 293 prefecture-level cities in China, and developed a three-dimensional framework of heat hazard, exposure, and vulnerability grounded in the risk framework developed by the Intergovernmental Panel on Climate Change. The results revealed a clear spatial gradient, with higher heat risk concentrated in northwestern China and lower risk in the southeast. Overall, 64% of cities fell into moderate to high-risk categories. Among landscape-related indicators, the building structure index, average height, and average volume AV were key factors associated with heat risk, accounting for 10%, 11%, and 17%, respectively. Partial dependence analysis further indicated nonlinear relationships between landscape patterns and heat risk. This study provides a scalable framework for assessing urban heat risk and offers useful insights for urban planning, particularly in data-scarce regions.</p>

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Urban heat risk assessment: a machine learning analysis of multidimensional morphology in 293 Chinese cities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月2日

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  • Jialu Tang,
  • Hao Hou,
  • Ronald C. Estoque,
  • Yuji Murayama,
  • Xinmin Zhang,
  • Ruci Wang,
  • Tangao Hu

摘要

Amid ongoing climate change, the increasing frequency of extreme heat events has become a major concern, particularly in urban areas where the urban heat island effect intensifies thermal risks. Previous studies have largely focused on individual cities, with limited attention to their relationship with landscape patterns. This study investigated the nonlinear relationships between landscape indicators and urban heat risk using machine learning, based on multi-source data from 293 prefecture-level cities in China, and developed a three-dimensional framework of heat hazard, exposure, and vulnerability grounded in the risk framework developed by the Intergovernmental Panel on Climate Change. The results revealed a clear spatial gradient, with higher heat risk concentrated in northwestern China and lower risk in the southeast. Overall, 64% of cities fell into moderate to high-risk categories. Among landscape-related indicators, the building structure index, average height, and average volume AV were key factors associated with heat risk, accounting for 10%, 11%, and 17%, respectively. Partial dependence analysis further indicated nonlinear relationships between landscape patterns and heat risk. This study provides a scalable framework for assessing urban heat risk and offers useful insights for urban planning, particularly in data-scarce regions.