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:人类流动研究的一个核心问题是移徙制度。一般而言,移徙制度的定义是两个或两个以上地点之间一段时间内相对稳定的人员流动的一套新颖方法,这些新兴系统预期会随着时间变化而变化,但它们最好包含一个可以从经验中发现的稳定的基本结构。已经作出一些值得注意的尝试,正式或非正式地界定移徙制度,然而,由于难以运作,这些系统受到限制,而且其界定移徙制度的方式忽视了移徙的起源/目的地方面和/或未能考虑到移徙动态。在这项工作中,我们提出了一套新颖的方法,即从信号处理和机器学习理论中得出时段(ST)联合集群(Spatio-thoral) 。为了证明它有效地描述了我们从1990至2018年美国各州之间的国内移徙制度,我们通过美国国内税收局提供了这一时期的相关数据。具体地说,我们集中进行三个说明性案例研究:(一) 美国首都地区,(二) 加利福尼亚州,以及(三) 路易斯安那,重点是2005年发现卡特里飓风等外来事件。最后,我们以讨论和限制为结论。