Evidence map›Paper›PMID 37679443›Full record

ArticleNature human behaviour2023

World's human migration patterns in 2000-2019 unveiled by high-resolution data.

Venla Niva, Alexander Horton, Vili Virkki, Matias Heino, Maria Kosonen, Marko Kallio, Pekka Kinnunen, Guy J Abel, Raya Muttarak, Maija Taka and 2 more

Abstract read
In one paragraph

Article in Nature human behaviour, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
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  5. Article
  6. Article
  7. Navigating data challenges in socioeconomic impact assessments of conservation regimes.Conservation biology : the journal of the Society for Conservation Biology · 2025
    Review
  8. Article
  9. Article
  10. New York State Climate Impacts Assessment Chapter 05: Ecosystems.Annals of the New York Academy of Sciences · 2024
    Article
  11. Article
  12. 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

12 authors.

Venla Niva *Water and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland. venla.niva@aalto.fi.ORCID 0000-0002-1456-4881
Alexander HortonWater and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland.ORCID 0000-0003-3067-7138
Vili VirkkiWater and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland.ORCID 0000-0002-2603-3420
Matias HeinoWater and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland.
Maria KosonenWater and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland.
Marko KallioWater and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland.ORCID 0000-0002-6917-7790
Pekka KinnunenWater and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland.ORCID 0000-0003-0373-2795
Guy J AbelInternational Institute for Applied Systems Analysis, Laxenburg, Austria.ORCID 0000-0002-4893-5687
Raya MuttarakDepartment of Statistical Sciences, University of Bologna, Bologna, Italy.
Maija TakaWater and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland.ORCID 0000-0002-6147-9137
Olli VarisWater and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland.
Matti Kummu *Water and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland. matti.kummu@aalto.fi.ORCID 0000-0001-5096-0163

Funding

Academy of Finland (Suomen Akatemia) 317320Academy of Finland (Suomen Akatemia) 339834EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 101002973EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 819202National Natural Science Foundation of China (National Science Foundation of China) 41950410572
6 · The paper itself

Abstract

Despite being a topical issue in public debate and on the political agenda for many countries, a global-scale, high-resolution quantification of migration and its major drivers for the recent decades remained missing. We created a global dataset of annual net migration between 2000 and 2019 (~10 km grid, covering the areas of 216 countries or sovereign states), based on reported and downscaled subnational birth (2,555 administrative units) and death (2,067 administrative units) rates. We show that, globally, around 50% of the world's urban population lived in areas where migration accelerated urban population growth, while a third of the global population lived in provinces where rural areas experienced positive net migration. Finally, we show that, globally, socioeconomic factors are more strongly associated with migration patterns than climatic factors. While our method is dependent on census data, incurring notable uncertainties in regions where census data coverage or quality is low, we were able to capture migration patterns not only between but also within countries, as well as by socioeconomic and geophysical zonings. Our results highlight the importance of subnational analysis of migration-a necessity for policy design, international cooperation and shared responsibility for managing internal and international migration.

Indexed as

Emigration and ImmigrationHuman MigrationHumansPopulation DynamicsSocioeconomic FactorsUrban Population

Identifiers

PMID37679443
PMCPMC10663150

What OpenQuestion holds

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Registered trials

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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.