Abel Guy J, DeWaard Jack, Ha Jasmine Trang, Almquist Zack W
Asian Demographic Research Institute, School of Sociology and Political Science, Shanghai University, Shanghai, China.
Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna), International Institute for Applied Systems Analysis, Laxenburg, Austria.
Popul Space Place. 2021 Apr;27(3). doi: 10.1002/psp.2432. Epub 2021 Feb 16.
Presently, there is no agreed upon data-driven approach for identifying the geographic boundaries of migration networks that international migration systems are ultimately manifested in. Drawing from research on community detection methods, we introduce and apply the Information Theoretic Community Detection Algorithm for identifying and studying the geographic boundaries of migration networks. Using a new set of estimates of country-to-country migration flows every 5 years from 1990 to 1995 to 2010-2015, we trace the form and evolution of international migration networks over the past 25 years. Consistent with the concept of dynamic stability, we show that the number, size and internal country compositions of international migration networks have been remarkably stable over time; however, we also document many short-term fluctuations. We conclude by reflecting on the spirit of our work in this paper, which is to promote consensus around tools and best practices for identifying and studying international migration networks.
目前,对于确定国际移民系统最终所呈现的移民网络的地理边界,尚无一种公认的数据驱动方法。借鉴社区检测方法的研究成果,我们引入并应用信息论社区检测算法来识别和研究移民网络的地理边界。利用1990 - 1995年至2010 - 2015年每5年一组的新的国家间移民流动估计数据,我们追溯了过去25年国际移民网络的形式和演变。与动态稳定性概念一致,我们表明国际移民网络的数量、规模和内部国家构成随时间推移一直保持显著稳定;然而,我们也记录了许多短期波动。最后,我们反思了本文工作的主旨,即促进围绕识别和研究国际移民网络的工具及最佳实践形成共识。