Comparing Regional Patterns of Individual Movement Using Corrected Mobility Entropy

ABSTRACT

In this paper, we propose a correction of the Mobility Entropy indicator (ME) used to describe the diversity of individual movement patterns as can be captured by data from mobile phones. We argue that a correction is necessary because standard calculations of ME show a structural dependency on the geographical density of observation points, rendering results biased and comparisons between regions incorrect. As a solution, we propose the Corrected Mobility Entropy (CME). We apply our solution to a French mobile phone dataset with ∼18.5 million users. Results show CME to be less correlated to cell-tower density (r = –0.17 instead of –0.59 for ME). As a spatial pattern of mobility diversity, we find CME values to be higher in suburban regions compared to their related urban centers, while both decrease considerably with lowering urban center sizes. Based on regression models, we find mobility diversity to relate to factors like income and employment. Additionally, using CME reveals the role of car use in relation to land use, which was not recognized when using ME values. Our solution enables a better description of individual mobility at a large scale, which has applications in official statistics, urban planning and policy, and mobility research.

KEYWORDS:

• Mobility Entropy
• CDR data
• mobility studies
• regional geography
• urban areas

Acknowledgments

The authors wish to thank Orange Labs, especially the SENSE department, for making the data available. We also like to acknowledge the Erasmus+ Traineeship program. Also, we would like to thank Gerard (Gerry) Wilkinson for his help in preparing the manuscript. Finally, we dedicate this paper to Rein Ahas, a dear friend and a valued colleague. We will miss him and his kind advice, offered on this paper and on other research. He was a mentor and a leader in the field whose loss will be felt by many.

Disclosure Statement

No potential conflict of interest was reported by the authors.

The underlying research materials for this article can be accessed at https://doi.org/10.17634/154300-80. Some data supporting this article is not openly available because of confidentiality considerations. Please contact Newcastle Research Data Service at [email protected] for further information..

Notes on Contributors

Maarten Vanhoof is a PhD candidate at the Open Lab in Newcastle University and Orange Labs in Paris. His research focuses on the use of mobile phone data for geographical research and official statistics.

Willem Schoors received his MSc degree in Geography (GIS and spatial modelling) from the Katholieke Universiteit Leuven, Belgium. He is currently employed at Geo Solutions, where he focuses on geospatial application development.

Anton Van Rompaey is a professor in Geography at KU Leuven, Belgium.

Thomas Ploetz is an associate professor at the School of Interactive Computing, Georgia Tech, USA.

Zbigniew Smoreda is a sociologist and researcher at Orange Labs’ Sociology and Economics of Networks and Services (SENSE) Department.

ORCID

Maarten Vanhoof http://orcid.org/0000-0001-9591-024X

Willem Schoors http://orcid.org/0000-0001-6383-844X

Anton Van Rompaey http://orcid.org/0000-0001-5435-6887

Thomas Ploetz http://orcid.org/0000-0002-1243-7563

Zbigniew Smoreda http://orcid.org/0000-0002-4047-7597

Notes

1 In the case of CDR data, observation points are equal o cell-towers, which is the spatial resolution in which the data is observed.

2 For an extensive literature review on the different applications of CDR data in research, see Blondel, Decuyper, and Krings, 2015.

3 The 2010 Urban Area classification is based on data collected between 2006 and 2010 in the national census survey.

4 Rememer that we want our ME measure to express the diversity of movement, not the absolute number of visited towers. A direct relation between both indicates an (unwanted) dependency induced by higher chances of visiting more cell-towers in areas with high cell-tower density, rather than an objective measurement of movement diversity.

5 Given the definition, movement patterns that are often repeated, like commuting back and forth between work and home, contribute little to the entropy, resulting in low entropy values. They are the opposite of a diverse movement pattern.

Additional information

Funding

Maarten Vanhoof is funded by the EPSRC Center for Doctoral Training in Digital Civics [grant no. EP/L016176/1].