Spatially-constrained clustering of geospatial features for heat vulnerability assessment of favelas in Rio de Janeiro
Provides a replicable data-driven framework for urban planners and public health officials to target heat vulnerability interventions in informal settlements globally.
The study develops a spatially-constrained clustering framework to assess heat vulnerability in Rio de Janeiro's favelas, identifying two typologies with systematic temperature differences of 2–3°C during extreme heat events, showing that flat-terrain favelas experience higher heat exposure.
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.