Empowering Urban Governance through Urban Science: Multi-scale Dynamics of Urban Systems Worldwide

TL;DR

This paper explores multi-scale dynamics of urban systems worldwide, emphasizing the importance of spatial interactions and evolutionary paths in modeling city growth and behavior for better urban governance.

Contribution

It introduces a framework for analyzing city systems beyond national boundaries, validating dynamic models across regions, and incorporating spatial and evolutionary factors.

Findings

Models effectively capture city size and growth distributions.

Spatial interactions are key to understanding urban dynamics.

Contextual factors reduce forecasting errors.

Abstract

The current science of cities can provide a useful foundation for future urban policies, provided that these proposals have been validated by correct observations of the diversity of situations in the world. However, international comparisons of the evolution of cities often produce uncertain results because national territorial frameworks are not always in strict correspondence with the dynamics of urban systems. We propose to provide various compositions of systems of cities to better take into account the dynamic networking of cities that go beyond regional and national territorial boundaries. Different models conceived for explaining city size and urban growth distributions enable to establish a correspondence between urban trajectories when observed at the level of cities and systems of cities. We test the validity and representativeness of several dynamic models of complex urban systems and their variations across regions of the world, at the macroscopic scale of systems of cities. The originality of the approach is in considering spatial interaction and evolutionary path dependence as major features in the general behavior of urban entities. The models studied include diverse and complementary processes, such as economic exchanges, diffusion of innovations and physical network flows. Complex systems' dynamics is in principle unpredictable, but contextualizing it regarding demographic, income and resource components may help in minimizing the forecasting errors.