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Detecting and Reducing Biases in Cellular-Based Mobility Data Sets

Department of Telematic Engineering, University Carlos III of Madrid, Avda. Universidad 30, E-28911 Leganés, Madrid, Spain
Author to whom correspondence should be addressed.
Entropy 2018, 20(10), 736;
Received: 30 August 2018 / Revised: 21 September 2018 / Accepted: 22 September 2018 / Published: 25 September 2018
(This article belongs to the Special Issue Information Theory and 5G Technologies)
PDF [2651 KB, uploaded 25 September 2018]


Correctly estimating the features characterizing human mobility from mobile phone traces is a key factor to improve the performance of mobile networks, as well as for mobility model design and urban planning. Most related works found their conclusions on location data based on the cells where each user sends or receives calls or messages, data known as Call Detail Records (CDRs). In this work, we test if such data sets provide enough detail on users’ movements so as to accurately estimate some of the most studied mobility features. We perform the analysis using two different data sets, comparing CDRs with respect to an alternative data collection approach. Furthermore, we propose three filtering techniques to reduce the biases detected in the fraction of visits per cell, entropy and entropy rate distributions, and predictability. The analysis highlights the need for contextualizing mobility results with respect to the data used, since the conclusions are biased by the mobile phone traces collection approach. View Full-Text
Keywords: human mobility; cell-based location; ping-pong effect; mobility data sets entropy; mobility data sets predictability human mobility; cell-based location; ping-pong effect; mobility data sets entropy; mobility data sets predictability

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Rodriguez-Carrion, A.; Garcia-Rubio, C.; Campo, C. Detecting and Reducing Biases in Cellular-Based Mobility Data Sets. Entropy 2018, 20, 736.

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