The Model City. Drivers and Mechanisms of Long-term Urban Evolution and Resilience

The past provides the most extensive and comprehensive record of the spectrum of human behaviours, adaptations and responses to change available to us. Its potential as an enormous body of comparative material, however, has yet to be fully realised.

The Model City aims to understand the drivers and mechanisms of long-term urban evolution through the study of the historical trajectories of past cities.

Focusing on the question of urban resilience, “why do some cities thrive while others fail?”, it will identify factors that predict city persistence over decades and centuries and evaluate social, environmental and economic mechanisms that increase or erode urban resilience.

The Model City will aggregate and synthesize large amounts of Deep Past Urban Data and apply analytical methods developed in urban studies to investigate fundamental forces governing the long-term dynamics of urban systems.

By deploying a swath of formal methods such as time-series analysis, ecological measures of resilience, network science, and agent-based modelling on a large and diverse sample of past cities for which we have full evolutionary trajectories, the project will identify factors, their interactions and processes that impact urban resilience.

In looking for universals, The Model City will compare three very distinctive but data-rich research areas from across the spectrum of past urban systems: Roman Empire - the first large highly integrated socio-political body in human history; Classic Mesoamerica - one of the earliest urbanised systems in tropical environments and Medieval Northern Europe - an early example of increasing globalisation driving urbanisation.

This comparative study will capture past urban variability to identify the most common causes of urban collapse as well as mechanisms that enabled past cities to survive for millennia and will unlock vast amounts of Deep Past Data to scholars across urban studies thus creating a new interdisciplinary research line.