通过空间约束聚类分析里约贫民窟热脆弱性，发现地形影响高温暴露差异。
Spatially-constrained clustering of geospatial features for heat vulnerability assessment of favelas in Rio de Janeiro
- 用空间约束聚类结合地表温度数据划分贫民窟类型。
- 平坦地形贫民窟比坡地社区高温高出2–3℃，极端天气下更易受热害。
- 方法可复制，适合城市规划与公共卫生干预参考。
非正式住区面临气候相关健康风险的不平等暴露。现有方法缺乏将多样社区特征与环境健康结果系统关联的途径。本文构建数据驱动框架，结合空间约束聚类与地表温度（LST）分析，评估里约热内卢贫民窟的热脆弱性。利用遥感与地理空间数据，识别出两类典型贫民窟：位于平坦地形、近期建成且连接度高的社区（簇0），以及位于植被覆盖坡地、历史形成且连接度低的社区（簇1）。对16次极端高温事件的分析显示，两簇间存在2–3℃的系统性温差，平坦地形贫民窟高温暴露显著更高。研究证明社区形态对热脆弱性有关键影响，为全球非正式住区的精准规划与公共健康干预提供了可复用的方法框架。
原文摘要 · Abstract (English)
Informal settlements face disproportionate exposure to climate-related health hazards. However, existing methodologies lack systematic approaches to link diverse settlement characteristics with environmental health outcomes. We develop a data-driven framework to assess heat vulnerability in Rio de Janeiro's favelas by combining spatially-constrained clustering with land surface temperature (LST) analysis. Using remote sensing and geospatial features, we identify two distinct favela typologies: recent, well-connected settlements on flat terrain (Cluster 0) and historical, poorly-connected communities on vegetated slopes (Cluster 1). Analysis of 16 extreme heat events reveals systematic temperature differences of 2--3$^\circ$C between clusters, with flat-terrain favelas experiencing significantly higher heat exposure. Our findings demonstrate that settlement morphology critically influences heat vulnerability, providing a replicable framework for targeted urban planning and public health interventions in informal settlements globally.
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