Geo-located Twitter as proxy for global mobility patterns

By Bartosz Hawelka, Izabela Sitko, Euro Beinat, Stanislav Sobolevsky, Pavlos Kazakopoulos, Carlo Ratti. Cartography and Geographic Information Science, 2013

685 citations recorded by the source corpus.

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.

Subfields: Marketing, Transportation

View Paper: https://www.semanticscholar.org/paper/8914a147125c4c6b29ea1071de75295d3d7e503a

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Citations in the corpus

Human mobility: Models and applications (2017, background): “These profiles can range from intra-urban routine trips to worldwide travel [65,66].”
Leveraging Facebook's Advertising Platform to Monitor Stocks of Migrants (2017, background): “…that leverage innovative Web data sources, like geo-located e-mail logins (Zagheni and Weber 2012; State, Weber, and Za-gheni 2013), Twitter tweets (Zagheni et al. 2014; Hawelka et al. 2014), and LinkedIn profiles (State et al. 2014), have expanded our ability to estimate migration rates and flows.”
From mobile phone data to the spatial structure of cities (2014, methodology): “Various data sources have been used such as car GPS [2, 3], RFIDs for collective transportation [4], and also data coming from social networks such as Twitter[5] or Foursquare[6].”
A multi-source dataset of urban life in the city of Milan and the Province of Trentino (2015, background): “…(ICT) services like Twitter and Foursquare introduces further opportunities for researchers to inspect quantitatively different aspects of human behaviour such as the social well-being of individuals and communities 19 , socio-economic status of geographical regions 20 , and people's mobility 21 .”