Fiadino, P;
Ponce-Lopez, V;
Antonio, J;
Torrent-Moreno, M;
D'Alconzo, A;
(2017)
Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the "Always Connected Era".
In:
Big-DAMA '17: Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks.
(pp. pp. 43-48).
Association for Computing Machinery (ACM): New York, NY, USA.
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Ponce Lopez_Call_Detail_Records_for_Human_Mobility_Studies__Taking_Stock_of_the_Situation_in_the___Always_Connected_Era__.pdf - Accepted Version Download (1MB) | Preview |
Abstract
The exploitation of cellular network data for studying human mobility has been a popular research topic in the last decade. Indeed, mobile terminals could be considered ubiquitous sensors that allow the observation of human movements on large scale without the need of relying on non-scalable techniques, such as surveys, or dedicated and expensive monitoring infrastructures. In particular, Call Detail Records (CDRs), collected by operators for billing purposes, have been extensively employed due to their rather large availability, compared to other types of cellular data (e.g., signaling). Despite the interest aroused around this topic, the research community has generally agreed about the scarcity of information provided by CDRs: the position of mobile terminals is logged when some kind of activity (calls, SMS, data connections) occurs, which translates in a picture of mobility somehow biased by the activity degree of users. By studying two datasets collected by a Nation-wide operator in 2014 and 2016, we show that the situation has drastically changed in terms of data volume and quality. The increase of flat data plans and the higher penetration of "always connected" terminals have driven up the number of recorded CDRs, providing higher temporal accuracy for users' locations.
| Type: | Proceedings paper |
|---|---|
| Title: | Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the "Always Connected Era" |
| Event: | Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (Big-DAMA '17) |
| Location: | Los Angeles, CA |
| Dates: | 21 August 2017 |
| ISBN-13: | 9781450350549 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3098593.3098601 |
| Publisher version: | https://doi.org/10.1145/3098593.3098601 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10111953 |
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