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A model of urban scaling laws based on distance dependent interactions. | LitMetric

A model of urban scaling laws based on distance dependent interactions.

R Soc Open Sci

EACH-Universidade de São Paulo (USP) , Av. Arlindo Bettio, 1000 (Vila Guaraciaba), 03828-000 São Paulo, SP, Brazil.

Published: March 2017

AI Article Synopsis

  • Socio-economic developments in cities increase at a rate faster than their population growth, indicating a non-linear relationship in log-log plots.
  • Larger cities utilize their infrastructure more efficiently, leading to improved socio-economic indicators influenced by the social interactions among citizens and the city's fractal properties.
  • The study introduces a "social potential" measure and suggests that enhancing interaction ranges among citizens can reduce infrastructure costs while benefiting urban development, aligning with theoretical predictions and empirical data.

Article Abstract

Socio-economic related properties of a city grow faster than a linear relationship with the population, in a log-log plot, the so-called . Conversely, the larger a city, the more efficient it is in the use of its infrastructure, leading to a on these variables. In this work, we addressed a simple explanation for those scaling laws in cities based on the interaction range between the citizens and on the fractal properties of the cities. To this purpose, we introduced a measure of social potential which captured the influence of social interaction on the economic performance and the benefits of amenities in the case of infrastructure offered by the city. We assumed that the population density depends on the fractal dimension and on the distance-dependent interactions between individuals. The model suggests that when the city interacts as a whole, and not just as a set of isolated parts, there is improvement of the socio-economic indicators. Moreover, the bigger the interaction range between citizens and amenities, the bigger the improvement of the socio-economic indicators and the lower the infrastructure costs of the city. We addressed how public policies could take advantage of these properties to improve cities development, minimizing negative effects. Furthermore, the model predicts that the sum of the scaling exponents of social-economic and infrastructure variables are 2, as observed in the literature. Simulations with an agent-based model are confronted with the theoretical approach and they are compatible with the empirical evidences.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5383838PMC
http://dx.doi.org/10.1098/rsos.160926DOI Listing

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