Urban Resilience Key Metrics Thinking and Computing Using 3D Spatio-Temporal Forecasting Algorithms

Résumé En

The vagueness of the concept of resilience makes it difficult to define unanimously, and it becomes even more problematic when it comes to measuring it, while urban resilience metrics can be considered as key indicators transmitting vital information to the decision makers on the observed characteristics about the city. The motivations and goals of such a metric are as different as the proponents who defend them. As for cities, due to growing urbanization in a global context of climate change, the concept of urban resilience is essential and requires scientific attention backed by a methodology with an operational aim. Based on 3D spatio-temporal forecasting algorithms, this paper revisits the concept and presents a novel approach to measuring and computing key metrics of resilience applied to urban systems. Some results show that spatio-temporal forecasting algorithms can significantly improve the accuracy and timeliness of urban resilience metrics compared to traditional methods. Our methodology can help urban planners and policymakers make more informed decisions and enhance the resilience of urban systems. However, the methodology also has limitations and challenges, such as data quality issues and algorithmic complexity, that require further research. This paper contributes to the literature on urban resilience and spatio-temporal forecasting by providing a comprehensive framework for measuring and forecasting key metrics of urban resilience using advanced computational methods.