A central problem in the study of human mobility is that of migration systems. Typically, migration systems are defined as a set of relatively stable movements of people between two or more locations over time. While these emergent systems are expected to vary over time, they ideally contain a stable underlying structure that could be discovered empirically. There have been some notable attempts to formally or informally define migration systems, however they have been limited by being hard to operationalize, and by defining migration systems in ways that ignore origin/destination aspects and/or fail to account for migration dynamics. In this work we propose a novel method, spatio-temporal (ST) tensor co-clustering, stemming from signal processing and machine learning theory. To demonstrate its effectiveness for describing stable migration systems we focus on domestic migration between counties in the US from 1990-2018. Relevant data for this period has been made available through the US Internal Revenue Service. Specifically, we concentrate on three illustrative case studies: (i) US Metropolitan Areas, (ii) the state of California, and (iii) Louisiana, focusing on detecting exogenous events such as Hurricane Katrina in 2005. Finally, we conclude with discussion and limitations of this approach.

翻译：人类流动研究中的一个核心问题是迁移系统。通常，迁移系统被定义为随时间推移，在两个或多个地点之间相对稳定的人口移动集合。尽管这些涌现系统预期会随时间变化，但它们理想情况下包含可通过经验发现的稳定底层结构。已有一些值得注意的尝试来正式或非正式地定义迁移系统，然而这些尝试因难以操作化，以及定义迁移系统时忽略起点/终点方面和/或未能考虑迁移动态而受到限制。在这项工作中，我们提出了一种源于信号处理和机器学习理论的新方法——时空张量共聚类。为证明其在描述稳定迁移系统方面的有效性，我们聚焦于1990-2018年间美国县际国内迁移。该时期的相关数据已通过美国国税局公开提供。具体而言，我们专注于三个说明性案例研究：（i）美国大都市区、（ii）加利福尼亚州和（iii）路易斯安那州，重点关注检测2005年卡特里娜飓风等外生事件。最后，我们讨论了该方法的局限性并作出总结。