Enhancing Urban Heatwave Response Planning via a Graph-Based Digital Twin Approach: Spatial Dependency Risk Analysis in Vienna City

Urban heatwaves pose an escalating threat to cities, requiring advanced planning strategies. This paper presents a graph-based digital twin approach to model spatial dependencies in heatwave risk for urban areas. Using Vienna’s city center as a case study, we integrate (i) high-resolution spatial data from Google on heat hazard, (ii) spatial population distribution from Vienna Open Data and(iii) META’s Vienna population datasets alongside (iv) critical road infrastructure from OpenStreetMaps, into a graph network model that captures cumulative risk paths that help determine the most critical city blocks needing intervention and the optimal allocation of resources for routing first responders to reach critical locations of sensitive populations. Our Spatial Dependency Risk Analysis identifies how the distribution of vulnerable population needing first aid in one location together with heat data may amplify risks in connected areas and provide a planning tool to first responders to draft optimal routes to reach all persons in need as fast as possible. The results show how city center junctions form interdependent high-risk clusters; notably, our tool managed to identify the most at-risk sub-district reaches and automatically plan ambulance routes from cooling spots to high risk hotspots. To our knowledge, this is the first automatic tool able to quantify these network routing effects during heatwave disasters, and enhance heatwave response planning by pinpointing critical areas where targeted interventions (e.g., cooling centers or outreach) could substantially reduce cascading impacts.