复杂的美国移民网络

Q1 Mathematics

Computational Social Networks Pub Date : 2019-01-24 DOI:10.1186/s40649-019-0061-6

Batyr Charyyev, Mehmet Hadi Gunes

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引用次数: 1

摘要

经济学家和社会科学家对人类迁徙进行了广泛的研究。然而，对美国复杂的人口流动网络研究并不深入。本文以2000 - 2015年美国县州之间的人口迁移网络为研究对象，采用时间分析法分析美国人口迁移的整体结构和年变化。我们在不同的时间窗口上聚合了网络，并对县和州两级进行了分析。本文分析了美国县州之间的人口流动，重点分析了房地产繁荣时期的经济繁荣和房地产萧条时期的经济困难时期的人口流动。我们观察到，县和州一级的节点通常保持活跃，但链接上有相当大的波动。这表明迁移模式随着时间的推移而改变。然而，我们可以使用视差过滤器来识别县和州级别的主干。最后，我们分析了政治和社会经济因素对移民的影响。利用引力模型，我们观察到人口、政治派别、贫困和失业率对美国移民的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Complex network of United States migration

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.

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来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.