World's human migration patterns in 2000-2019 unveiled by high-resolution data.
Journal
Nature human behaviour
ISSN: 2397-3374
Titre abrégé: Nat Hum Behav
Pays: England
ID NLM: 101697750
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
05
07
2022
accepted:
01
08
2023
medline:
23
11
2023
pubmed:
8
9
2023
entrez:
7
9
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37679443
doi: 10.1038/s41562-023-01689-4
pii: 10.1038/s41562-023-01689-4
pmc: PMC10663150
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2023-2037Subventions
Organisme : Academy of Finland (Suomen Akatemia)
ID : 339834
Organisme : Academy of Finland (Suomen Akatemia)
ID : 317320
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : 819202
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : 101002973
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 41950410572
Informations de copyright
© 2023. The Author(s).
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