Assessing Urban Vulnerability to Emergencies: A Spatiotemporal Approach Using K-Means Clustering

Today, urban areas across the world are increasingly vulnerable to emergencies due to expanding populations and the impact of climate change. This paper presents a data-driven method for assessing the susceptibility of urban regions to emergencies, using publicly available data and a clustering-based algorithm. The study incorporates both spatial and temporal dynamics, capturing the fluctuating nature of urban infrastructure and patterns of human movement over time. By introducing the notion of Points of Temporal Influence (PTIs) and a new “susceptibility level” parameter, the proposed model offers an innovative approach to understanding urban susceptibility. Experiments conducted in London, the UK, demonstrated the effectiveness of the Spatiotemporal K-means Clustering algorithm in identifying areas with heightened time-sensitive susceptibility. The findings highlight the value of incorporating both spatial and temporal data to enhance emergency response strategies and optimize urban planning efforts. This study contributes to the literature on smart cities by providing a scalable and adaptable method for improving urban resilience in the face of evolving challenges.