Evidence map›Paper›PMID 27271481›Full record

ArticleScientific data2016

Spatializing 6,000 years of global urbanization from 3700 BC to AD 2000.

Meredith Reba, Femke Reitsma, Karen C Seto

Abstract readDataset
In one paragraph

Article in Scientific data, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–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

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Attention in Urban and Natural Environments.The Yale journal of biology and medicine · 2019
    Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Can the NHS learn from the rise and fall of ancient empires?Journal of the Royal Society of Medicine · 2017
    Article
  18. Article
  19. Review
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

3 authors.

Meredith RebaYale School of Forestry &Environmental Studies, Yale University, New Haven, Connecticut 06511, USA.
Femke ReitsmaDepartment of Geography, University of Canterbury, Christchurch 8020, New Zealand.
Karen C SetoYale School of Forestry &Environmental Studies, Yale University, New Haven, Connecticut 06511, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

How were cities distributed globally in the past? How many people lived in these cities? How did cities influence their local and regional environments? In order to understand the current era of urbanization, we must understand long-term historical urbanization trends and patterns. However, to date there is no comprehensive record of spatially explicit, historic, city-level population data at the global scale. Here, we developed the first spatially explicit dataset of urban settlements from 3700 BC to AD 2000, by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data previously published in tabular form by Chandler and Modelski. The dataset creation process also required data cleaning and harmonization procedures to make the data internally consistent. Additionally, we created a reliability ranking for each geocoded location to assess the geographic uncertainty of each data point. The dataset provides the first spatially explicit archive of the location and size of urban populations over the last 6,000 years and can contribute to an improved understanding of contemporary and historical urbanization trends.

Indexed as

DemographyUrbanizationUrban PopulationCitiesHumansPopulation DensityPopulation Dynamics

Identifiers

PMID27271481
PMCPMC4896125

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.