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Explaining the prevalence, scaling and variance of urban phenomena

Author

Listed:
  • Andres Gomez-Lievano

    (Center for International Development, Harvard University)

  • Oscar Patterson-Lomba

    (Harvard T.H. Chan School of Public Health, Harvard University)

  • Ricardo Hausmann

    (Center for International Development, Harvard University
    Santa Fe Institute
    Harvard Kennedy School, Harvard University)

Abstract

The prevalence of many urban phenomena changes systematically with population size1. We propose a theory that unifies models of economic complexity2,3 and cultural evolution4 to derive urban scaling. The theory accounts for the difference in scaling exponents and average prevalence across phenomena, as well as the difference in the variance within phenomena across cities of similar size. The central ideas are that a number of necessary complementary factors must be simultaneously present for a phenomenon to occur, and that the diversity of factors is logarithmically related to population size. The model reveals that phenomena that require more factors will be less prevalent, scale more superlinearly and show larger variance across cities of similar size. The theory applies to data on education, employment, innovation, disease and crime, and it entails the ability to predict the prevalence of a phenomenon across cities, given information about the prevalence in a single city.

Suggested Citation

  • Andres Gomez-Lievano & Oscar Patterson-Lomba & Ricardo Hausmann, 2017. "Explaining the prevalence, scaling and variance of urban phenomena," Nature Human Behaviour, Nature, vol. 1(1), pages 1-6, January.
    • Handle: RePEc:nat:nathum:v:1:y:2017:i:1:d:10.1038_s41562-016-0012
    DOI: 10.1038/s41562-016-0012
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    Cited by:

    1. Chong, Shi Kai & Bahrami, Mohsen & Chen, Hao & balcisoy, Selim & Bozkaya, Burcin & Pentland, Alex 'Sandy', 2020. "Economic outcomes predicted by diversity in cities," OSF Preprints j59u3, Center for Open Science.
    2. Eduardo Lora, 2016. "The Path to Labor Formality: Urban Agglomeration and the Emergence of Complex Industries," CID Working Papers 78, Center for International Development at Harvard University.
    3. Ghosh, Abhik & Mallick, Olivia & Chattopadhay, Souvik & Basu, Banasri, 2022. "Strata-based quantification of distributional uncertainty in socio-economic indicators: A comparative study of Indian states," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    4. Tasnuva Mahjabin & Susana Garcia & Caitlin Grady & Alfonso Mejia, 2018. "Large cities get more for less: Water footprint efficiency across the US," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-17, August.
    5. Mewes, Lars & Broekel, Tom, 2022. "Technological complexity and economic growth of regions," Research Policy, Elsevier, vol. 51(8).
    6. Koen Frenken & Frank Neffke & Alje van Dam, 2023. "Capabilities, institutions and regional economic development: a proposed synthesis," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 16(3), pages 405-416.
    7. Hadi Arbabi & Gregory Meyers & Ling-Min Tan & Martin Mayfield, 2022. "Comment on Bettignies et al. The Scale-Dependent Behaviour of Cities: A Cross-Cities Multiscale Driver Analysis of Urban Energy Use. Sustainability 2019, 11 , 3246," Sustainability, MDPI, vol. 14(7), pages 1-6, April.

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