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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2604.26133 |
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| _version_ | 1866910176064307200 |
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| author | Clemence, Baptiste Hallopeau, Thomas De Matos, Vanderlei Pascoal Demagistri, Laurent Guerin, Joris |
| author_facet | Clemence, Baptiste Hallopeau, Thomas De Matos, Vanderlei Pascoal Demagistri, Laurent Guerin, Joris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26133 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spatially-constrained clustering of geospatial features for heat vulnerability assessment of favelas in Rio de Janeiro Clemence, Baptiste Hallopeau, Thomas De Matos, Vanderlei Pascoal Demagistri, Laurent Guerin, Joris Machine Learning 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. |
| title | Spatially-constrained clustering of geospatial features for heat vulnerability assessment of favelas in Rio de Janeiro |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.26133 |