Patterns of Urban Infastructure Resilience

Disasters and shocks have long-term implications for infrastructure, urban growth and socio-economic development. In this project, we explore co-evolution of critical infrastructures using socio-spatial datasets and machine learning techniques to expand our understanding of socio-technical and environmental dynamics in cities. Thereby, the inherent path-dependencies,interdependencies in social-technical-environmental systems as well as the capability of recovery from unexpected shocks and stresses characterised in terms of urban resilience. This novel approach and method allows us to bridge the resilience engineering perspective, focusing on rapid recovery, with the socio-ecological perspectives that stress adaptation and transformation. Our method will allow decision-makers to integrate the longer-term consequences of a shock into longer-term urban planning and development.