Next Article in Journal
Coarse Graining, Nonmaximal Entropy, and Power Laws
Next Article in Special Issue
A New Belief Entropy to Measure Uncertainty of Basic Probability Assignments Based on Belief Function and Plausibility Function
Previous Article in Journal
Data Driven Approach to the Dynamics of Import and Export of G7 Countries
Previous Article in Special Issue
Dynamic Virtual Network Reconfiguration Method for Hybrid Multiple Failures Based on Weighted Relative Entropy
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle

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; https://doi.org/10.3390/e20100736
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]
  |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top