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Uncovering migration systems through spatio-temporal tensor co-clustering.

Zack W Almquist1, Tri Duc Nguyen2, Mikael Sorensen3

  • 1Departments of Sociology and Statistics, University of Washington, Seattle, WA, 98195, USA. zalmquist@uw.edu.

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This study introduces spatio-temporal tensor co-clustering to analyze migration systems. This novel method reveals stable patterns in human mobility, improving our understanding of migration dynamics over time.

Keywords:
ClusteringDynamic clusteringMigrationNetwork scienceSocial networksTensors

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Area of Science:

  • Human mobility studies
  • Computational social science
  • Data science

Background:

  • Migration systems are typically defined as stable population movements between locations.
  • Existing definitions are difficult to operationalize and often overlook origin/destination dynamics and temporal changes.
  • A robust analytical tool is needed to empirically discover and understand migration system structures.

Purpose of the Study:

  • To introduce spatio-temporal tensor co-clustering as a novel tool for analyzing migration systems.
  • To demonstrate the method's ability to identify stable underlying structures in human mobility data.
  • To apply the technique to both domestic US and international migration data.

Main Methods:

  • Employed spatio-temporal tensor co-clustering, a machine learning technique.
  • Analyzed domestic US county-to-county migration data (1990-2018).
  • Examined international migration data from 200 countries (1990-2015).

Main Results:

  • Successfully applied tensor co-clustering to identify migration system structures in US domestic data.
  • Case studies on US Metropolitan Areas, California, and Louisiana demonstrated the method's utility.
  • Analysis of international migration data showcased the approach's scalability.

Conclusions:

  • Spatio-temporal tensor co-clustering is an effective tool for analyzing migration systems.
  • The method can uncover stable migration patterns and dynamics over time.
  • This approach offers a novel way to empirically study human mobility.