Geo-located Twitter as proxy for global mobility patterns

Pervasive presence of location-sharing services made it possible for researchers to gain an unprecedented access to the direct records of human activity in space and time. This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of...

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Bibliographic Details
Main Authors: Hawelka, Bartosz (Contributor), Sitko, Izabela (Contributor), Beinat, Euro (Author), Sobolevsky, Stanislav (Contributor), Kazakopoulos, Pavlos (Author), Ratti, Carlo (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Urban Studies and Planning (Contributor), Massachusetts Institute of Technology. SENSEable City Laboratory (Contributor)
Format: Article
Language:English
Published: Cartography & Geographic Information Society, 2016-03-09T16:57:19Z.
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Online Access:Get fulltext
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100 1 0 |a Hawelka, Bartosz  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Urban Studies and Planning  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. SENSEable City Laboratory  |e contributor 
100 1 0 |a Hawelka, Bartosz  |e contributor 
100 1 0 |a Sitko, Izabela  |e contributor 
100 1 0 |a Sobolevsky, Stanislav  |e contributor 
100 1 0 |a Ratti, Carlo  |e contributor 
700 1 0 |a Sitko, Izabela  |e author 
700 1 0 |a Beinat, Euro  |e author 
700 1 0 |a Sobolevsky, Stanislav  |e author 
700 1 0 |a Kazakopoulos, Pavlos  |e author 
700 1 0 |a Ratti, Carlo  |e author 
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520 |a Pervasive presence of location-sharing services made it possible for researchers to gain an unprecedented access to the direct records of human activity in space and time. This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012, we estimate the volume of international travelers by country of residence. Mobility profiles of different nations were examined based on such characteristics as mobility rate, radius of gyration, diversity of destinations, and inflow-outflow balance. Temporal patterns disclose the universally valid seasons of increased international mobility and the particular character of international travels of different nations. Our analysis of the community structure of the Twitter mobility network reveals spatially cohesive regions that follow the regional division of the world. We validate our result using global tourism statistics and mobility models provided by other authors and argue that Twitter is exceptionally useful for understanding and quantifying global mobility patterns. 
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