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Updated: Jul 17, 2025

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
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通过高分辨率数据揭示了2000-2019年世界人类迁徙模式.
Venla Niva1, Alexander Horton2, Vili Virkki2
1Water and Environmental Engineering Research Group, School of Engineering, Aalto University, Espoo, Finland. venla.niva@aalto.fi.
Nature human behaviour
|September 7, 2023
概括
这项研究量化了2000年至2019年的全球移民模式,揭示了社会经济因素驱动的移动比气候更重要. 对于有效的移民政策和管理而言,地方分析至关重要.
科学领域:
- 人口统计学 人口统计学
- 人类地理的人类地理
- 社会经济学 社会经济学
背景情况:
- 全球移民是一个重要的公共和政治问题,但缺乏关于其规模和驱动力的高分辨率数据.
- 了解移民模式对于解决全球人口动态和资源分配至关重要.
研究的目的:
- 创建一个高分辨率的全球数据集2000年至2019年的年度净迁移.
- 在全球范围内识别和分析移民的主要驱动因素.
- 突出地方移民分析对政策和国际合作的重要性.
主要方法:
- 开发了216个国家的全球净移民数据集 (~10公里网格),使用缩小规模的次国家出生和死亡率.
- 利用报告的人口普查数据和行政单位信息进行人口分析.
- 分析了与社会经济和地质物理因素相关的移民模式.
主要成果:
- 在2000年至2019年期间,全球量化年度净迁移.
- 发现社会经济因素比气候因素更强烈地与移民有关.
- 观察到,大约50%的城市人口生活在移民推动增长的地区,全球三分之一的人口居住在农村净移民积极的省份.
结论:
- 对于有效的政策设计和管理来说,对移民的地方性分析至关重要.
- 社会经济驱动因素在移民中扮演着比气候因素更重要的角色.
- 开发的数据集为了解和管理内部和国际移民提供了宝贵的资源.
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