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I know where you live: Inferring details of people's lives by visualizing publicly shared location data

Abstract:

This research measures human performance in inferring the functional types (i.e., home, work, leisure and transport) of locations in geo-location data using different visual representations of the data (textual, static and animated visualizations) along with different amounts of data (1, 3 or 5 day(s)).


We first collected real life geo-location data from tweets. We then asked the data owners to tag their location points, resulting in ground truth data. Using this dataset we co...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1145/2858036.2858272

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Oxford e-Research Centre
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Oxford e-Research Centre
Role:
Author
Publisher:
Association for Computing Machiney Publisher's website
Host title:
ACM SIGCHI Conference on Human Factors in Computing Systems 2016 (CHI)
Publication date:
2016-05-01
DOI:
Source identifiers:
612157
Keywords:
Pubs id:
pubs:612157
UUID:
uuid:bcac1512-4c35-4f9e-8868-d79422ee52ab
Local pid:
pubs:612157
Deposit date:
2016-03-29

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