Geo-located Twitter as proxy for global mobility patterns

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.

Tourists' digital footprint in cities: Comparing Big Data sources

“…The very few works that use geolocated tweets in the field of tourism tend to focus on comparing visitor's spatial behaviour between cities on the national or global scale (Bassolas et al, 2016;Hawelka et al, 2014;Sobolevsky et al, 2015), but do not address the spatial patterns within the city.…”

Location privacy without mutual trust: The spatial Bloom filter

“…As evident from the graphs, in the case of the Brussels area, any hash mapping m ≥ 2 18 will provide a probability of identifying patterns close to 0, while in the case of Belgium this is true for values of m ≥ 2 26 .…”

“…Examples of such location-aware services are local advertising, traffic or weather information, or suggestions about points of interest (PoI) in the user's surroundings [6]. Even existing services are now improved by the addition of locationbased data: notable example are social networks [18] or retail distribution [11].…”

Estimates of Global Bilateral Migration Flows by Gender between 1960 and 20151

“…Native born stocks are likely to lower in younger countries, where the sum of years lived, and hence the cumulative time available to people to emigrate, are lower. Recent studies of global migration patterns such as Hawelka et al (2014); State, Weber, and Zagheni (2013); Zagheni and Weber (2012) or Zagheni et al (2014) have focused on short term mobility measures derived from data sources based on individuals geolocated of internet activities such as twitter messages or logins to email services. As the authors note, the data in these may not be fully representative of the whole global population.…”