  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.
