Mobility and sociocultural events in mobile phone data records
The massive amounts of geolocation data collected from mobile phone records have sparked an ongoing effort to understand and predict the mobility patterns of human beings. In this work, we study the extent to which social phenomena are reflected in mobile phone data, providing some proof-of-principl...
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2016
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paper:paper_09217126_v29_n1_p77_Ponieman2023-06-08T15:50:47Z Mobility and sociocultural events in mobile phone data records Human mobility human predictability social phenomena Mobile phones Telephone sets Automatic Detection Human mobility human predictability Mobile phone datum Mobile-phone users Mobility pattern Proof of principles social phenomena Cellular telephones The massive amounts of geolocation data collected from mobile phone records have sparked an ongoing effort to understand and predict the mobility patterns of human beings. In this work, we study the extent to which social phenomena are reflected in mobile phone data, providing some proof-of-principle examples. We illustrate in various ways how these events are reflected in the data, and show how information about the events can be used to improve predictability in a simple model for a mobile phone user's location. We further propose a method for the automatic detection of such events and discuss their relation to the social fabric as derived from mobile phone communications. © 2016 - IOS Press and the authors. All rights reserved. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09217126_v29_n1_p77_Ponieman http://hdl.handle.net/20.500.12110/paper_09217126_v29_n1_p77_Ponieman |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Human mobility human predictability social phenomena Mobile phones Telephone sets Automatic Detection Human mobility human predictability Mobile phone datum Mobile-phone users Mobility pattern Proof of principles social phenomena Cellular telephones |
spellingShingle |
Human mobility human predictability social phenomena Mobile phones Telephone sets Automatic Detection Human mobility human predictability Mobile phone datum Mobile-phone users Mobility pattern Proof of principles social phenomena Cellular telephones Mobility and sociocultural events in mobile phone data records |
topic_facet |
Human mobility human predictability social phenomena Mobile phones Telephone sets Automatic Detection Human mobility human predictability Mobile phone datum Mobile-phone users Mobility pattern Proof of principles social phenomena Cellular telephones |
description |
The massive amounts of geolocation data collected from mobile phone records have sparked an ongoing effort to understand and predict the mobility patterns of human beings. In this work, we study the extent to which social phenomena are reflected in mobile phone data, providing some proof-of-principle examples. We illustrate in various ways how these events are reflected in the data, and show how information about the events can be used to improve predictability in a simple model for a mobile phone user's location. We further propose a method for the automatic detection of such events and discuss their relation to the social fabric as derived from mobile phone communications. © 2016 - IOS Press and the authors. All rights reserved. |
title |
Mobility and sociocultural events in mobile phone data records |
title_short |
Mobility and sociocultural events in mobile phone data records |
title_full |
Mobility and sociocultural events in mobile phone data records |
title_fullStr |
Mobility and sociocultural events in mobile phone data records |
title_full_unstemmed |
Mobility and sociocultural events in mobile phone data records |
title_sort |
mobility and sociocultural events in mobile phone data records |
publishDate |
2016 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09217126_v29_n1_p77_Ponieman http://hdl.handle.net/20.500.12110/paper_09217126_v29_n1_p77_Ponieman |
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1768541944462442496 |