Open Access
Dissecting global air traffic data to discern different types and trends of transnational human mobility
Loading...
Files
Gabrielli2019_Article_DissectingGlobalAirTrafficData.pdf (3.13 MB)
Full-text in Open Access
License
Attribution 4.0 International
Cadmus Permanent Link
Full-text via DOI
ISBN
ISSN
2193-1127
Issue Date
Type of Publication
LC Subject Heading
Other Topic(s)
EUI Research Cluster(s)
Initial version
Published version
Succeeding version
Preceding version
Published version part
Earlier different version
Initial format
Citation
EPJ Data Science, 2019, Vol. 8, Art. 26, OnlineOnly
[Migration Policy Centre]; [Global Mobilities Project]
Cite
GABRIELLI, Lorenzo, DEUTSCHMANN, Emanuel, NATALE, Fabrizio, RECCHI, Ettore, VESPE, Michele, Dissecting global air traffic data to discern different types and trends of transnational human mobility, EPJ Data Science, 2019, Vol. 8, Art. 26, OnlineOnly, [Migration Policy Centre], [Global Mobilities Project] - https://hdl.handle.net/1814/64106
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.