Venezuelan migration flows estimated from geolocated Twitter data match official statistics at country level, with high-resolution maps of routes and settlement.
Twitter as a Source of Global Mobility Patterns for Social Good
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abstract
Data on human spatial distribution and movement is essential for understanding and analyzing social systems. However existing sources for this data are lacking in various ways; difficult to access, biased, have poor geographical or temporal resolution, or are significantly delayed. In this paper, we describe how geolocation data from Twitter can be used to estimate global mobility patterns and address these shortcomings. These findings will inform how this novel data source can be harnessed to address humanitarian and development efforts.
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physics.soc-ph 1years
2019 1verdicts
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Migrant mobility flows characterized with digital data
Venezuelan migration flows estimated from geolocated Twitter data match official statistics at country level, with high-resolution maps of routes and settlement.