Evidence map›Paper›PMID 27493764›Full record

ArticleRoyal Society open science2016

Is this scaling nonlinear?

J C Leitão, J M Miotto, M Gerlach, E G Altmann

Open access · goldAbstract read
In one paragraph

Article in Royal Society open science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 1 pooled it
49.0field-weighted citation impact, top 1% of its field
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

43 citing papers in PubMed, 1 synthesis or guideline pooled it, 117 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. The coherence of US cities.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Scaling COVID-19 rates with population size in the United States.Journal of the Royal Society, Interface · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. How the geometry of cities determines urban scaling laws.Journal of the Royal Society, Interface · 2021
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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

4 authors at 1 institution in 1 country.

J C LeitãoMax Planck Institute for the Physics of Complex Systems , Dresden, Germany.ORCID 0000-0003-1503-9242
J M MiottoMax Planck Institute for the Physics of Complex Systems , Dresden, Germany.ORCID 0000-0002-5850-3394
M GerlachMax Planck Institute for the Physics of Complex Systems , Dresden, Germany.ORCID 0000-0002-0879-7865
E G AltmannMax Planck Institute for the Physics of Complex Systems , Dresden, Germany.ORCID 0000-0002-1932-3710
Max Planck Institute for the Physics of Complex Systems · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the most celebrated findings in complex systems in the last decade is that different indexes y (e.g. patents) scale nonlinearly with the population x of the cities in which they appear, i.e. y∼x (β) ,β≠1. More recently, the generality of this finding has been questioned in studies that used new databases and different definitions of city boundaries. In this paper, we investigate the existence of nonlinear scaling, using a probabilistic framework in which fluctuations are accounted for explicitly. In particular, we show that this allows not only to (i) estimate β and confidence intervals, but also to (ii) quantify the evidence in favour of β≠1 and (iii) test the hypothesis that the observations are compatible with the nonlinear scaling. We employ this framework to compare five different models to 15 different datasets and we find that the answers to points (i)-(iii) crucially depend on the fluctuations contained in the data, on how they are modelled, and on the fact that the city sizes are heavy-tailed distributed.

Indexed as

allometryscaling lawsstatistical inference

Identifiers

PMID27493764
PMCPMC4968456
OpenAlexW2341046739

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.