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The hypothesis of urban scaling: formalization, implications and challenges

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arxiv 1301.5919 v1 pith:ZPCDAVSS submitted 2013-01-24 physics.soc-ph nlin.AO

classification physics.soc-phnlin.AO
keywords citiesscalingurbanhypothesispropertiessystemsdiscussevidence
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There is strong expectation that cities, across time, culture and level of development, share much in common in terms of their form and function. Recently, attempts to formalize mathematically these expectations have led to the hypothesis of urban scaling, namely that certain properties of all cities change, on average, with their size in predictable scale-invariant ways. The emergence of these scaling relations depends on a few general properties of cities as social networks, co-located in space and time, that conceivably apply to a wide range of human settlements. Here, we discuss the present evidence for the hypothesis of urban scaling, some of the methodological issues dealing with proxy measurements and units of analysis and place these findings in the context of other theories of cities and urban systems. We show that a large body of evidence about the scaling properties of cities indicates, in analogy to other complex systems, that they cannot be treated as extensive systems and discuss the consequences of these results for an emerging statistical theory of cities.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 50 citations worldwide. Full citation record

  1. The third dimension of cities - relating building height, urban area, and population

    physics.soc-ph 2025-08 reject novelty 5.0 of 10

    Across 2,903 cities, population size tracks total building area but essentially not average building height, implying vertical development adds little capacity for residents.

  2. Tomography of scaling

    physics.soc-ph 2019-08 conditional novelty 5.0 of 10

    A pairwise local exponent and a benchmark-city effective exponent are proposed as fitting-free diagnostics for scaling, yielding new insights on urban datasets.

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