2017/12/31 by Vicky Chuqiao Yang, Andrew V. Papachristos, Daniel M. Abrams · 30 citations
Biochemistry, Genetics and Molecular Biology · Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #COVID-19 epidemiological studies #Cartography #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Computer science #Demography #Econometrics #Geography #Geometry #Mathematics #Mechanism (biology) #Phenomenon #Physics #Population #Power law #Process (computing) #Scale (ratio) #Scaling #Scaling law #Sociology #Statistical physics #Statistics #physics.soc-ph #q-bio.PE
paper · pdf · doi:10.1103/physreve.100.032306
published in Physical review. E 100(3), 032306 (American Physical Society) · 8 pages, 5 figures
arxiv created 2019/08/26 · openalex publication_date 2019/09/16 · arxiv updated 2019/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Urban outputs often scale superlinearly with city population. A difficulty in understanding the mechanism of this phenomenon is that different outputs differ considerably in their scaling behaviors. Here, we formulate a physics-based model for the origin of superlinear scaling in urban outputs by treating human interaction as a random process. Our model suggests that the increased likelihood of finding required collaborations in a larger population can explain this superlinear scaling, which our model predicts to be non-power-law. Moreover, the extent of superlinearity should be greater for activities that require more collaborators. We test this model using a novel dataset for seven crime types and find strong support.