Money Circulation, Trackable Items, and the Emergence of Universal Human Mobility Patterns
IEEE Pervasive Computing
Digital Footprinting: Uncovering Tourists with User-Generated Content
IEEE Pervasive Computing
A case for space: physical and virtual location requirements in the CouchSurfing social network
Proceedings of the 2009 International Workshop on Location Based Social Networks
Investigating the imprecision of IP block-based geolocation
PAM'07 Proceedings of the 8th international conference on Passive and active network measurement
Constructing travel itineraries from tagged geo-temporal breadcrumbs
Proceedings of the 19th international conference on World wide web
Automatic construction of travel itineraries using social breadcrumbs
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Planet-scale human mobility measurement
Proceedings of the 2nd ACM International Workshop on Hot Topics in Planet-scale Measurement
How to tell an airport from a home: techniques and applications
Hotnets-IX Proceedings of the 9th ACM SIGCOMM Workshop on Hot Topics in Networks
IP geolocation databases: unreliable?
ACM SIGCOMM Computer Communication Review
Unveiling the complexity of human mobility by querying and mining massive trajectory data
The VLDB Journal — The International Journal on Very Large Data Bases
Extracting urban patterns from location-based social networks
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
You are where you e-mail: using e-mail data to estimate international migration rates
Proceedings of the 3rd Annual ACM Web Science Conference
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The increasing ubiquity of Internet use has opened up new avenues in the study of human mobility. Easily-obtainable geolocation data resulting from repeated logins to the same website offer the possibility of observing long-term patterns of mobility for a large number of individuals. We use data on the geographic locations from where over 100 million anonymized users log into Yahoo!~services to generate the first global map of short- and medium-term mobility flows. We develop a protocol to identify anonymized users who, over a one-year period, had spent more than 3 months in a different country from their stated country of residence ("migrants"), and users who spent less than a month in another country ("tourists"). We compute aggregate estimates of migration propensities between countries, as inferred from a user's location over the observed period. Geolocation data allow us to characterize also the pendularity of migration flows -- i.e., the extent to which migrants travel back and forth between their countries of origin and destination. We use data regarding visa regimes, colonial ties, geographic location and economic development to predict migration and tourism flows. Our analysis shows the persistence of traditional migration patterns as well as the emergence of new routes. Migrations tend to be more pendular between countries that are close to each other. We observe particularly high levels of pendularity within the European Economic Area, even after we control for distance and visa regimes. The dataset, methodology and results presented have important implications for the travel industry, as well as for several disciplines in social sciences, including geography, demography and the sociology of networks.