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Data on human spatial distribution and movement is essential forunderstanding and analyzing social systems. However existing sources for thisdata are lacking in various ways; difficult to access, biased, have poorgeographical or temporal resolution, or are significantly delayed. In thispaper, we describe how geolocation data from Twitter can be used to estimateglobal mobility patterns and address these shortcomings. These findings willinform how this novel data source can be harnessed to address humanitarian anddevelopment efforts.

Twitter as a Source of Global Mobility Patterns for Social Good