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Cartography and Geographic Information Science· 2014Q2

Geo-located Twitter as proxy for global mobility patterns

Bartosz Hawelka, Izabela Sitko, Euro Beinat, Stanislav Sobolevsky et al.

Short summary

A 2012 dataset of nearly a billion geo-located tweets reveals global human mobility patterns, estimating international traveler volumes by country of residence and identifying distinct national mobility profiles and temporal travel seasons.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Nearly 1 billion geo-located tweets from 2012 were analyzed to map global mobility.
  • International traveler volumes were estimated by country of residence.
  • Mobility profiles included rate, radius of gyration, destination diversity, and inflow-outflow balance.
  • Analysis identified universal seasons of increased international travel and distinct national travel characteristics.
  • Results were validated using global tourism statistics and other mobility models.

AI-generated from the title and abstract; the full text is not read.

Abstract

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.

The authors' abstract, as published at the source. Cartography and Geographic Information Science, 2014 · DOI ↗

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Field: Transportation

TransportationSocial Sciences