Spatially-constrained clustering of geospatial features for heat vulnerability assessment of favelas in Rio de Janeiro

TL;DR

This paper presents a data-driven geospatial clustering framework to assess heat vulnerability in Rio de Janeiro's favelas, revealing how settlement morphology influences heat exposure.

Contribution

It introduces a novel spatially-constrained clustering method combined with land surface temperature analysis for identifying favela typologies related to heat vulnerability.

Findings

Flat-terrain favelas experience 2-3°C higher temperatures than hillside communities.

Two distinct favela typologies are identified based on morphology and connectivity.

Settlement morphology significantly impacts heat exposure and vulnerability.

Abstract

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.