Rapid urbanization and global warming are amplifying heat-related health risks, particularly for vulnerable age groups. This study develops an open-source risk assessment framework that uses big data from remote sensing, land use, and population datasets to evaluate heat-related health risks. The framework integrates indicators of green infrastructure, Land Surface Temperature (LST), and demographic vulnerability to identify areas of increased health risk. Milan (Italy) was used as the case study for the application to test the methodology and validate its capacity to detect spatial correlations between Surface Urban Heat Island (Surface UHI) intensity and concentrations of sensitive population groups (children aged 0–5 and elderly aged 65+). The results highlight distinct spatial inequalities in heat exposure and health vulnerability, confirming the method’s potential to support climate adaptation and public health planning. By relying entirely on open-access data and tools, this approach offers a replicable and scalable model for assessing climate-related health risks and informing evidence-based strategies that can support public administrations to visualize risk, prioritize interventions, and enhance urban resilience.
Buffoli et al. (Mon,) studied this question.
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