Year: 2016
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Dredze, Mark, García-Herranz, Manuel, Rutherford, Alex, Mann, Gideon
Institutions: Bloomberg (United States), Johns Hopkins University
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2471787768