Evidence map›Paper›PMID 33481927›Full record

ArticlePloS one2021

Association between population distribution and urban GDP scaling.

Haroldo V Ribeiro, Milena Oehlers, Ana I Moreno-Monroy, Jürgen P Kropp, Diego Rybski

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Large cities lose their growth advantage as countries urbanize.Proceedings of the National Academy of Sciences of the United States of America · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Haroldo V RibeiroDepartamento de Física, Universidade Estadual de Maringá, Maringá, PR, Brazil.ORCID 0000-0002-9532-5195
Milena OehlersPotsdam Institute for Climate Impact Research - PIK, Member of Leibniz Association, Potsdam, Germany.
Ana I Moreno-MonroyOECD, Paris, France.
Jürgen P KroppPotsdam Institute for Climate Impact Research - PIK, Member of Leibniz Association, Potsdam, Germany.
Diego RybskiPotsdam Institute for Climate Impact Research - PIK, Member of Leibniz Association, Potsdam, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban scaling and Zipf's law are two fundamental paradigms for the science of cities. These laws have mostly been investigated independently and are often perceived as disassociated matters. Here we present a large scale investigation about the connection between these two laws using population and GDP data from almost five thousand consistently-defined cities in 96 countries. We empirically demonstrate that both laws are tied to each other and derive an expression relating the urban scaling and Zipf exponents. This expression captures the average tendency of the empirical relation between both exponents, and simulations yield very similar results to the real data after accounting for random variations. We find that while the vast majority of countries exhibit increasing returns to scale of urban GDP, this effect is less pronounced in countries with fewer small cities and more metropolises (small Zipf exponent) than in countries with a more uneven number of small and large cities (large Zipf exponent). Our research puts forward the idea that urban scaling does not solely emerge from intra-city processes, as population distribution and scaling of urban GDP are correlated to each other.

Indexed as

CitiesPopulation DensityModels, Statistical

Identifiers

PMID33481927
PMCPMC7822261

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.