Assessing Urban Vulnerability and Resilience to Extreme Heat with Big Data and Machine Learning: The Case of July 2019 European Heatwave

Climate change has put residents of many cities around the world at risk. The challenge for urban planners is not only to understand which people are vulnerable and where they are located but also assess how resilient the city is to climatic shocks. To assess vulnerability and resilience, we proposed a framework based on open data and open-source machine learning libraries. We applied this framework to analyze the impact of July 2019 European heatwave on The Hague, the Netherlands. The framework combines a geodemographic grid of 500 by 500 m2 with 3,973,549 anonymized ambulance calls to capture citizens' vulnerability and street networks with distance matrices for evaluation of resilience as accessibility by critical infrastructure. We found that vulnerability and resilience are unequally distributed both spatially and temporally. People who are the most sensitive to extreme heat such as low-income single households with kids and elderly with mobility issues are also exposed the most. The lack of greenery in their residential areas resulted in urban heat island effect which may have amplified the impact of the heatwave. Such a combination represents high vulnerability and has led to an increase in ambulance calls by 17% from the yearly average. The south and northwest parts of the city where the population is dominated by above mentioned groups by 75% are the furthest from the hospitals and as a result less resilient. In addition to an increase in calls, new peak hours appeared at 12:00, 16:00 and 02:00 where intense afternoon and evening traffic make these citizens hardly reachable when they are the most vulnerable. Knowledge of spatial and temporal variability in vulnerability and resilience can provide policy-makers with insight about potential interventions. Our proposed framework can be generalized and applied to other cities with similar data availability.