Hi all, Anyone who's interested in converting free-text location fields into standardized location data might want to look at a Python module I designed to do just that: https://github.com/dfreelon/geostring You simply feed it a list of locations and it tries to guess where each one is at the country, sub-country (state/province), and city levels. I'm interested to see how people might use it. Best, /DEEN On 2/22/2019 10:23 AM, Sugar, Benjamin N wrote:
Is that 100% of geolocated Tweets in the hours before a disaster? That would be an interesting idea, but a tough one to execute.
Stu’s point is very true. When the GPS data was not available in the Tweet, we used the location field of the Twitter user’s profile and OpenStreetMap’s Nominatim which gives (or used to give) you a probability of where your query was located.
Of course, not all users have an accurate location field,some are jokes or fictitious places (e.g. Hogwarts) but we got enough state level data for a paper out of it.
https://github.com/openstreetmap/Nominatim
https://wiki.openstreetmap.org/wiki/Nominatim _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
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-- Deen Freelon, Ph.D. Associate Professor School of Media and Journalism, UNC-Chapel Hill http://dfreelon.org | @dfreelon <https://twitter.com/dfreelon> | https://github.com/dfreelon