Dissecting global air traffic data to discern different types and trends of transnational human mobility

Abstract

              Human mobility across national borders is a key phenomenon of our time. At the global scale, however, we still know relatively little about the structure and nature of such transnational movements. This study uses a large dataset on monthly air passenger traffic between 239 countries worldwide from 2010 to 2018 to gain new insights into (a) mobility trends over time and (b) types of mobility. A time series decomposition is used to extract a trend and a seasonal component. The trend component permits—at a higher level of granularity than previous sources—to examine the development of mobility between countries and to test how it is affected by policy and infrastructural changes, economic developments, and violent conflict. The seasonal component allows, by measuring the lag between initial and return motion, to discern different types of mobility, from tourism to seasonal work migration. Moreover, the exact shape of seasonal mobility patterns is extracted, allowing to identify regular mobility peaks and nadirs throughout the year. The result is a unique classification of trends and types of mobility for a global set of country pairs. A range of implications and possible applications are discussed. Open-Access-Publikationsfonds 2019

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• http://link.springer.com/article/10.1140/epjds/s13688-019-0204-x,

https://doaj.org/toc/2193-1127

• http://link.springer.com/content/pdf/10.1140/epjds/s13688-019-0204-x.pdf,

http://link.springer.com/article/10.1140/epjds/s13688-019-0204-x/fulltext.html,

http://dx.doi.org/10.1140/epjds/s13688-019-0204-x under the license https://creativecommons.org/licenses/by/4.0

• https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-019-0204-x,

https://cadmus.eui.eu/handle/1814/64106,

https://link.springer.com/content/pdf/10.1140/epjds/s13688-019-0204-x.pdf,

https://link.springer.com/article/10.1140/epjds/s13688-019-0204-x,

https://epjdatascience.springeropen.com/track/pdf/10.1140/epjds/s13688-019-0204-x,

https://epjds.epj.org/articles/epjdata/abs/2019/01/13688_2019_Article_204/13688_2019_Article_204.html,

https://dblp.uni-trier.de/db/journals/epjds/epjds8.html#GabrielliDNRV19,

https://doi.org/10.1140/epjds/s13688-019-0204-x,

https://academic.microsoft.com/#/detail/2971071711

• https://spire.sciencespo.fr/hdl:/2441/537klsou7g9tabon8hglreov2b,

https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-019-0204-x,

https://spire.sciencespo.fr/hdl:/2441/537klsou7g9tabon8hglreov2b/resources/gabrielli2019-article-dissectingglobalairtrafficdata.pdf

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DOIS: 10.1140/epjds/s13688-019-0204-x 10.1140/epjds/s13688-019-0204-x/fulltext.html 10.1140/epjds/s13688-019-0204-x.pdf