  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.
