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高分辨率数据揭示了 2000-2019 年全球人口迁移模式。

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

机构信息

Water and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland.

Geoinformatics Research Group, School of Engineering, Aalto University, Espoo, Finland.

出版信息

Nat Hum Behav. 2023 Nov;7(11):2023-2037. doi: 10.1038/s41562-023-01689-4. Epub 2023 Sep 7.

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.

摘要

尽管移民问题在公众辩论和许多国家的政治议程中都是一个热门话题,但对于最近几十年的移民及其主要驱动因素,仍缺乏全球性、高分辨率的量化分析。我们创建了一个涵盖 2000 年至 2019 年期间年度净移民数据的全球数据集(~10 公里网格,覆盖 216 个国家或主权国家的地区),该数据集基于报告和下推的次国家出生率(2555 个行政区)和死亡率(2067 个行政区)数据。研究表明,在全球范围内,大约有 50%的世界城市人口生活在移民加速城市人口增长的地区,而全球三分之一的人口生活在农村地区经历净移民增长的省份。最后,我们发现,在全球范围内,社会经济因素与移民模式的相关性强于气候因素。虽然我们的方法依赖于人口普查数据,但在人口普查数据覆盖或质量较低的地区会产生显著的不确定性,但我们不仅能够捕捉到国家之间的移民模式,也能够捕捉到国家内部的移民模式,以及社会经济和地理分区的移民模式。我们的研究结果强调了对移民进行次国家分析的重要性,这是政策制定、国际合作以及共同承担管理国内和国际移民的责任的必要条件。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2243/10663150/1c0d7445c4ec/41562_2023_1689_Fig1_HTML.jpg

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