通过时空张量共同集群来发现迁移系统.
Zack W Almquist1, Tri Duc Nguyen2, Mikael Sorensen3
1Departments of Sociology and Statistics, University of Washington, Seattle, WA, 98195, USA. zalmquist@uw.edu.
Scientific reports
|November 5, 2024
概括
这项研究引入了时空张量协集群来分析迁移系统. 这种新的方法揭示了人类流动的稳定模式,改善了我们对随时间推移的迁移动态的理解.
科学领域:
- 人类流动性的研究研究.
- 计算社会科学 计算社会科学
- 数据科学是数据科学.
背景情况:
- 迁移系统通常被定义为地点之间的稳定人口流动.
- 现有的定义很难运行,并且经常忽视来源/目的地动态和时间变化.
- 需要一个强大的分析工具来经验地发现和理解迁移系统结构.
研究的目的:
- 引入时空张量协集群作为分析迁移系统的新工具.
- 证明该方法能够在人类流动性数据中识别稳定的基础结构的能力.
- 将该技术应用于美国国内和国际移民数据.
主要方法:
- 采用时空张量协集,一种机器学习技术.
- 分析了美国国内县到县的移民数据 (1990-2018).
- 研究了来自200个国家的国际移民数据 (1990-2015年).
主要成果:
- 成功地应用了张量协集群来识别美国国内数据中的迁移系统结构.
- 美国大都市地区,加利福尼亚州和路易斯安那州的案例研究证明了该方法的实用性.
- 对国际移民数据的分析显示了该方法的可扩展性.
结论:
- 时空张量协集是分析迁移系统的有效工具.
- 该方法可以发现稳定的迁移模式和随时间推移的动态.
- 这种方法提供了一种新的方法来经验地研究人类的流动性.
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