Abstract
Economists and social scientists have studied the human migration extensively. However, the complex network of human mobility in the United States (US) is not studied in depth. In this paper, we analyze migration network between counties and states in the US between 2000 and 2015 to analyze the overall structure of US migration and yearly changes using temporal analysis. We aggregated network on different time windows and analyzed for both county and state level. Analyzing flow between US counties and states, we focus on the migration during different periods such as economic prosperity of the housing boom and economic hardship of the housing bust. We observed that nodes at county and state level usually remain active, but there are considerable fluctuations on links. This indicates that migration patterns change over the time. However, we could identify a backbone at both county and state levels using disparity filter. Finally, we analyze impact of the political and socioeconomic factors on the migration. Using gravity model, we observe that population, political affiliation, poverty, and unemployment rate have influence on US migration.
Highlights
Research has shown that there is a correlation between economic growth and net migration as people seek opportunities [1]
As United States (US) economy has become primarily driven on services and technology, the migration patterns have changed [4]
Charyyev and Gunes Comput Soc Netw (2019) 6:1 Table 3 Evolution of yearly state-level migration network backbones than other counties in 2010, it is the top county with respect to most of the centrality measures
Summary
Research has shown that there is a correlation between economic growth and net migration as people seek opportunities [1]. To analyze different social and economic factors, most studies use gravity model for migration. These studies focus on factors like infant mortality rate, education quality, political view, and income growth. [19] analyzes dynamics of link utilization using records of communication in a large social network They observed that roles of nodes change dramatically from day to day, and authors assert that interventions targeting hubs will have significantly less effect than previously thought. Bajardi et al [21] examines dynamic patterns of cattle trade movements They aggregate cattle trade movement data into different time windows and study temporal properties of the network. They found that centrality of nodes fluctuate strongly over the time which hinders assessment of the spreading potential of premises
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
