Year: 2017
Type: article
Abstract: Although most of wealth and innovation have been the result of human interaction and cooperation, we are not yet able to quantitatively predict the spatial distributions of three main elements of cities: population, roads, and socioeconomic interactions. By a simple model mainly based on spatial attraction and matching growth mechanisms, we reveal that the spatial scaling rules of these three elements are in a consistent framework, which allows us to use any single observation to infer the others. All numerical and theoretical results are consistent with empirical data from ten representative cities. In addition, our model can also provide a general explanation of the origins of the universal super- and sub-linear aggregate scaling laws and accurately predict kilometre-level socioeconomic activity. Our work opens a new avenue for uncovering the evolution of cities in terms of the interplay among urban elements, and it has a broad range of applications.
Source: Nature Communications
Institutions: Boston University, Beijing Normal University, Tsinghua University
Language: English
Subfield: Urban Studies
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: National Science Foundation, U.S. Department of Energy, Arizona State University, Santa Fe Institute, University College London, National Natural Science Foundation of China, Beijing Normal University, Defense Threat Reduction Agency, Fundamental Research Funds for the Central Universities, Division of Civil, Mechanical and Manufacturing Innovation, Division of Chemistry
Awards: G1736866159, G1890829241, G2619044695, Collaborative Research: Unraveling Cerebral Connectivity with Diffusion MRI, Microscopy and Statistical Physics, 自组织视角下的社会集群行为涌现机制和演化规律研究, Collaborative Reseach: Ordering Process in Water, Aqueous Solutions, and Water-Biomolecule Systems, CDI-Type II: Collaborative Research: Dynamical processes in interdependent techno-social networks, G4371387823, G4925338744, G5293159114, G6541015203, G702394665, G7520120651
OpenAlex ID: W2769918300