使用在线数据测量全球移民流动
Guanghua Chi1, Guy J Abel2,3, Drew Johnston4
1Meta, Menlo Park, CA 94025.
概括
来自30亿Facebook用户的新数据提供了可靠的,实时的全球人类移民估计. 这项研究为国际人口流动提供了及时的见解,对政策和人道主义努力至关重要.
科学领域:
- 人口统计学 人口统计学
- 计算社会科学 计算社会科学
- 数据科学数据科学数据科学
背景情况:
- 现有的人类迁移数据缺乏范围,可靠性和及时性.
- 联合国和"全球移民契约"等国际组织主张加强数据收集方法.
研究的目的:
- 开发一种新的方法来估计从一个国家到另一个国家的人类移民流.
- 提供近乎实时的全球移民数据,每月提供细节.
主要方法:
- 利用了30亿Facebook用户的隐私保护数据.
- 开发了一个模型来估计移民流动,考虑到Facebook用户的选择偏差.
- 与高质量的现有迁移数据相对应的验证估计.
主要成果:
- 估计2022年有3910万国际移民 (0.63%的样本人口).
- 在COVID-19大流行期间,观察到移民减少了64%,随后在2022年比危机前的比例增加了24%.
- 记录了俄罗斯入侵后乌克兰移民的十倍增长.
结论:
- 隐私保护的社交媒体数据为全球移民估计提供了可扩展和及时的解决方案.
- 移民模式是动态的,受到全球事件 (如流行病和地缘政治冲突) 的重大影响.
- 通过人道主义数据交换对这些估计的公开发布支持研究和政策干预.
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