Emergence of Urban Growth Patterns from Human Movements

Cities grow in a bottom-up manner, leading to fractal-like urban morphology characterized by scaling laws. Correlated percolation has succeeded in modeling urban geometries by imposing strong spatial correlations. However, the origin of such correlations remains largely unknown. Very recently, our understanding of human movements has been revolutionized thanks to the increasing availability of large-scale human mobility data. This paper designs a novel computational urban growth model that offers a micro-foundation in human mobility behavior. We compare the proposed model with three empirical datasets, which evidences that strong social couplings and long-memory effect are two fundamental principles responsible for the mystical spatial correlations. The model not only accounts for the empirically observed scaling laws, but also allows us to understand the city evolution dynamically.