Creating full individual-level location timelines from sparse social media data
In many domain applications, a continuous timeline of human locations is critical; for
example for understanding possible locations wherea disease may spread, or the flow of
traffic. While data sources such as GPS trackers or Call Data Records are temporally-rich,
they are expensive, often not publicly available or garnered only in select locations,
restricting their wide use. Conversely, geo-located social media data are publicly and freely
available, but present challenges especially for full timeline inference due to their sparse …
example for understanding possible locations wherea disease may spread, or the flow of
traffic. While data sources such as GPS trackers or Call Data Records are temporally-rich,
they are expensive, often not publicly available or garnered only in select locations,
restricting their wide use. Conversely, geo-located social media data are publicly and freely
available, but present challenges especially for full timeline inference due to their sparse …
Creating Full Individual-level Location Timelines from Sparse Social Media Data
N Abdur Rehman, K Relia, R Chunara - arXiv e-prints, 2017 - ui.adsabs.harvard.edu
In many domain applications, a continuous timeline of human locations is critical; for
example for understanding possible locations where a disease may spread, or the flow of
traffic. While data sources such as GPS trackers or Call Data Records are temporally-rich,
they are expensive, often not publicly available or garnered only in select locations,
restricting their wide use. Conversely, geo-located social media data are publicly and freely
available, but present challenges especially for full timeline inference due to their sparse …
example for understanding possible locations where a disease may spread, or the flow of
traffic. While data sources such as GPS trackers or Call Data Records are temporally-rich,
they are expensive, often not publicly available or garnered only in select locations,
restricting their wide use. Conversely, geo-located social media data are publicly and freely
available, but present challenges especially for full timeline inference due to their sparse …
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