Open Access

Saskia Rupp, Tobias Leichtle, Christoph Beck, Michael Hiete

• With rising temperatures and ongoing demographic shifts, heat-related health risks are expected to intensify in the coming decades, highlighting the need for tailored adaptation and mitigation strategies – especially in densely populated urban areas. This study presents a transferable approach for constructing a Heat Risk Index (HRI) using open-source data and tools to support urban stakeholders in identifying and addressing heat vulnerability. The HRI integrates climatological data (hazard), population distribution (exposure), and socio-demographic factors (sensitivity) to assess spatial heat risk patterns across urban areas. Within three model cities, population groups were identified by conducting a Latent Class Analysis (LCA) based on social vulnerability data. Three latent classes (LCs) were consistently found across cities: “Young & Diverse”, “Adults & Citizens” and “Elderly & Single-Household”. Heat risk was distributed unevenly among these groups and spatially within each city.With rising temperatures and ongoing demographic shifts, heat-related health risks are expected to intensify in the coming decades, highlighting the need for tailored adaptation and mitigation strategies – especially in densely populated urban areas. This study presents a transferable approach for constructing a Heat Risk Index (HRI) using open-source data and tools to support urban stakeholders in identifying and addressing heat vulnerability. The HRI integrates climatological data (hazard), population distribution (exposure), and socio-demographic factors (sensitivity) to assess spatial heat risk patterns across urban areas. Within three model cities, population groups were identified by conducting a Latent Class Analysis (LCA) based on social vulnerability data. Three latent classes (LCs) were consistently found across cities: “Young & Diverse”, “Adults & Citizens” and “Elderly & Single-Household”. Heat risk was distributed unevenly among these groups and spatially within each city. By mapping both heat exposure and social vulnerability, the study offers a practical tool for risk mitigation in urban planning. While this approach enhances understanding heat-related vulnerability, it faces limitations related to data resolution, model assumptions, and static representations of risk. Further research should explore whether similar risk groups can be identified across multiple cities, or whether variations in urban structure – such as infrastructural and social – lead to different patterns of heat risk within cities. Additionally, adapting the HRI to different seasons or daytimes or developing personas for each population group to establish a more advanced planning tool for local stakeholders in heat risk management could be a different future research approach.…