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World’s human migration patterns in 2000–2019 unveiled by high-resolution data

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...

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Autores principales: Niva, Venla, Horton, Alexander, Virkki, Vili, Heino, Matias, Kosonen, Maria, Kallio, Marko, Kinnunen, Pekka, Abel, Guy J., Muttarak, Raya, Taka, Maija, Varis, Olli, Kummu, Matti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663150/
https://www.ncbi.nlm.nih.gov/pubmed/37679443
http://dx.doi.org/10.1038/s41562-023-01689-4
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author Niva, Venla
Horton, Alexander
Virkki, Vili
Heino, Matias
Kosonen, Maria
Kallio, Marko
Kinnunen, Pekka
Abel, Guy J.
Muttarak, Raya
Taka, Maija
Varis, Olli
Kummu, Matti
author_facet Niva, Venla
Horton, Alexander
Virkki, Vili
Heino, Matias
Kosonen, Maria
Kallio, Marko
Kinnunen, Pekka
Abel, Guy J.
Muttarak, Raya
Taka, Maija
Varis, Olli
Kummu, Matti
author_sort Niva, Venla
collection PubMed
description 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.
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spelling pubmed-106631502023-09-07 World’s human migration patterns in 2000–2019 unveiled by high-resolution data Niva, Venla Horton, Alexander Virkki, Vili Heino, Matias Kosonen, Maria Kallio, Marko Kinnunen, Pekka Abel, Guy J. Muttarak, Raya Taka, Maija Varis, Olli Kummu, Matti Nat Hum Behav Resource 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. Nature Publishing Group UK 2023-09-07 2023 /pmc/articles/PMC10663150/ /pubmed/37679443 http://dx.doi.org/10.1038/s41562-023-01689-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Resource
Niva, Venla
Horton, Alexander
Virkki, Vili
Heino, Matias
Kosonen, Maria
Kallio, Marko
Kinnunen, Pekka
Abel, Guy J.
Muttarak, Raya
Taka, Maija
Varis, Olli
Kummu, Matti
World’s human migration patterns in 2000–2019 unveiled by high-resolution data
title World’s human migration patterns in 2000–2019 unveiled by high-resolution data
title_full World’s human migration patterns in 2000–2019 unveiled by high-resolution data
title_fullStr World’s human migration patterns in 2000–2019 unveiled by high-resolution data
title_full_unstemmed World’s human migration patterns in 2000–2019 unveiled by high-resolution data
title_short World’s human migration patterns in 2000–2019 unveiled by high-resolution data
title_sort world’s human migration patterns in 2000–2019 unveiled by high-resolution data
topic Resource
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663150/
https://www.ncbi.nlm.nih.gov/pubmed/37679443
http://dx.doi.org/10.1038/s41562-023-01689-4
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