# Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities

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## Abstract

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## 1. Introduction

#### 1.1. The Single-City Focus of Urban Sensing

#### 1.2. Mobile Phone Indicators

#### 1.3. Sensitivity of Urban Scaling Laws to City Definitions

#### 1.4. Research Question and Relevance

## 2. Data

#### 2.1. Census Data on Segregation, Inequality, and Deprivation

#### 2.2. Constructing Indicators from the French Mobile Phone Data

## 3. Methods

#### 3.1. Aggregation: From Cell Towers to Commune to City

#### 3.2. Simulating City Definitions

#### 3.3. Correlations between Mobile Phone Indicators and Census Data

#### 3.4. Representing Results for All 4914 City Definitions

#### 3.5. Clustering City Definitions into 6 Classes

## 4. Results

#### 4.1. Distributions of Correlation Coefficients for All 4914 City Definitions

#### 4.2. Distributions of Correlation Coefficients by Definition Cluster

#### 4.3. Heatmaps of Correlations

#### 4.4. Multiple Regression of Mobile Phone Indices with Socioeconomic Indicators

## 5. Discussion & Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Gonzalez, M.C.; Hidalgo, C.A.; Barabasi, A.L. Understanding individual human mobility patterns. Nature
**2008**, 453, 779–782. [Google Scholar] [CrossRef] [PubMed] - Zhong, C.; Batty, M.; Manley, E.; Wang, J.; Wang, Z.; Chen, F.; Schmitt, G. Variability in regularity: Mining temporal mobility patterns in London, Singapore and Beijing using smart-card data. PLoS ONE
**2016**, 11, e0149222. [Google Scholar] [CrossRef] [PubMed] - Lenormand, M.; Louail, T.; Cantú-Ros, O.G.; Picornell, M.; Herranz, R.; Arias, J.M.; Barthelemy, M.; San Miguel, M.; Ramasco, J.J. Influence of sociodemographic characteristics on human mobility. Sci. Rep.
**2015**, 5, 10075. [Google Scholar] [CrossRef] [PubMed] - De Montjoye, Y.A.; Radaelli, L.; Singh, V.K. Unique in the shopping mall: On the reidentifiability of credit card metadata. Science
**2015**, 347, 536–539. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Alessandretti, L.; Sapiezynski, P.; Sekara, V.; Lehmann, S.; Baronchelli, A. Evidence for a conserved quantity in human mobility. Nat. Hum. Behav.
**2018**, 2, 485–491. [Google Scholar] [CrossRef] [Green Version] - Pappalardo, L.; Simini, F.; Rinzivillo, S.; Pedreschi, D.; Giannotti, F.; Barabási, A.L. Returners and explorers dichotomy in human mobility. Nat. Commun.
**2015**, 6, 8166. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Batran, M.; Mejia, M.; Kanasugi, H.; Sekimoto, Y.; Shibasaki, R. Inferencing Human Spatiotemporal Mobility in Greater Maputo via Mobile Phone Big Data Mining. ISPRS Int. J. Geo-Inf.
**2018**, 7, 259. [Google Scholar] [CrossRef] - Lu, S.; Fang, Z.; Zhang, X.; Shaw, S.L.; Yin, L.; Zhao, Z.; Yang, X. Understanding the representativeness of mobile phone location data in characterizing human mobility indicators. ISPRS Int. J. Geo-Inf.
**2017**, 6, 7. [Google Scholar] [CrossRef] - Vanhoof, M.; Reis, F.; Ploetz, T.; Smoreda, Z. Assessing the quality of home detection from mobile phone data for official statistics. J. Off. Stat.
**2018**, 34, 935–960. [Google Scholar] [CrossRef] - Longley, P.A.; Adnan, M.; Lansley, G. The geotemporal demographics of Twitter usage. Environ. Plan. A
**2015**, 47, 465–484. [Google Scholar] [CrossRef] - Arai, A.; Fan, Z.; Matekenya, D.; Shibasaki, R. Comparative perspective of human behavior patterns to uncover ownership bias among mobile phone users. ISPRS Int. J. Geo-Inf.
**2016**, 5, 85. [Google Scholar] [CrossRef] - Schneider, C.M.; Belik, V.; Couronné, T.; Smoreda, Z.; González, M.C. Unravelling daily human mobility motifs. J. R. Soc. Interface
**2013**, 10, 20130246. [Google Scholar] [CrossRef] [PubMed] - Dannamann, T.; Sotomayor-Gómez, B.; Samaniego, H. The time geography of segregation during working hours. arXiv, 2018; arXiv:1802.00117. [Google Scholar] [CrossRef] [PubMed]
- Xu, Y.; Belyi, A.; Bojic, I.; Ratti, C. Human mobility and socioeconomic status: Analysis of Singapore and Boston. Comput. Environ. Urban Syst.
**2018**, 72, 51–67. [Google Scholar] [CrossRef] - Vanhoof, M.; Schoors, W.; Van Rompaey, A.; Ploetz, T.; Smoreda, Z. Comparing Regional Patterns of Individual Movement Using Corrected Mobility Entropy. J. Urban Technol.
**2018**, 25, 27–61. [Google Scholar] [CrossRef] - Vanhoof, M.; Lee, C.; Smoreda, Z. Performance and sensitivities of home detection from mobile phone data. arXiv, 2018; arXiv:1809.09911. [Google Scholar]
- Pappalardo, L.; Vanhoof, M.; Gabrielli, L.; Smoreda, Z.; Pedreschi, D.; Giannotti, F. An analytical framework to nowcast well-being using mobile phone data. Int. J. Data Sci. Anal.
**2016**, 2, 75–92. [Google Scholar] [CrossRef] [Green Version] - Eagle, N.; Macy, M.; Claxton, R. Network diversity and economic development. Science
**2010**, 328, 1029–1031. [Google Scholar] [CrossRef] - Decuyper, A.; Rutherford, A.; Wadhwa, A.; Bauer, J.M.; Krings, G.; Gutierrez, T.; Blondel, V.D.; Luengo-Oroz, M.A. Estimating food consumption and poverty indices with mobile phone data. arXiv, 2014; arXiv:1412.2595. [Google Scholar]
- Frias-martinez, V.; Soto, V.; Virseda, J.; Frias-martinez, E. Can cell phone traces measure social development. In Third Conference on the Analysis of Mobile Phone datasets, NetMob; Blondel, V., Decuyper, A., Deville, P., De Montjoye, Y.-A., Toole, J., Traag, V., Wang, D., Eds.; MIT Media Lab: Cambridge, MA, USA, 2013; pp. 62–65. [Google Scholar]
- Arcaute, E.; Hatna, E.; Ferguson, P.; Youn, H.; Johansson, A.; Batty, M. Constructing cities, deconstructing scaling laws. J. R. Soc. Interface
**2015**, 12, 20140745. [Google Scholar] [CrossRef] - Veneri, P. City size distribution across the OECD: Does the definition of cities matter? Comput. Environ. Urban Syst.
**2016**, 59, 86–94. [Google Scholar] [CrossRef] - Cottineau, C.; Hatna, E.; Arcaute, E.; Batty, M. Diverse cities or the systematic paradox of Urban Scaling Laws. Comput. Environ. Urban Syst.
**2017**, 63, 80–94. [Google Scholar] [CrossRef] [Green Version] - Louail, T.; Lenormand, M.; Arias, J.M.; Ramasco, J.J. Crowdsourcing the Robin Hood effect in cities. Appl. Netw. Sci.
**2017**, 2, 11. [Google Scholar] [CrossRef] [PubMed] - Shelton, T.; Poorthuis, A.; Zook, M. Social media and the city: Rethinking urban socio-spatial inequality using user-generated geographic information. Landsc. Urban Plan.
**2015**, 142, 198–211. [Google Scholar] [CrossRef] - Pornet, C.; Delpierre, C.; Dejardin, O.; Grosclaude, P.; Launay, L.; Guittet, L.; Lang, T.; Launoy, G. Construction of an adaptable European transnational ecological deprivation index: The French version. J. Epidemiol. Commun. Health
**2012**, 66, 982–989. [Google Scholar] [CrossRef] [PubMed] - Cottineau, C.; Finance, O.; Hatna, E.; Arcaute, E.; Batty, M. Defining urban clusters to detect agglomeration economies. Environ. Plan. B Urban Anal. City Sci.
**2018**. [Google Scholar] [CrossRef] - Fuller, M. The estimation of Gini coefficients from grouped data: Upper and Lower Bounds. Econ. Lett.
**1979**, 3, 187–192. [Google Scholar] [CrossRef] - Reardon, S.F. Measures of ordinal segregation. In Occupational and Residential Segregation; Research on Economic Inequality; Flückiger, Y., Reardon, S.F., Silber, J., Eds.; Emerald Group Publishing Limited: Bingley, UK, 2009; pp. 129–155. [Google Scholar]
- Grauwin, S.; Szell, M.; Sobolevsky, S.; Hövel, P.; Simini, F.; Vanhoof, M.; Smoreda, Z.; Barabási, A.L.; Ratti, C. Identifying and modeling the structural discontinuities of human interactions. Sci. Rep.
**2017**, 7, 46677. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Vanhoof, M.; Hendrickx, L.; Puussaar, A.; Verstraeten, G.; Ploetz, T.; Smoreda, Z. Exploring the use of mobile phones during domestic tourism trips. Netcom
**2017**, 31, 335–372. [Google Scholar] [CrossRef] - Vanhoof, M.; Combes, S.; De Bellefon, M.P. Mining mobile phone data to detect urban areas. In Statistics and Data Science: New Challenges, New Generations, SIS 2017; Petrucci, A., Verde, R., Eds.; Firenze University Press: Firenze, Italy, 2017; pp. 1005–1012. [Google Scholar]
- De Montjoye, Y.A.; Rocher, L.; Pentland, A.S. Bandicoot: A python toolbox for mobile phone metadata. J. Mach. Learn. Res.
**2016**, 17, 6100–6104. [Google Scholar] - Vanhoof, M.; Reis, F.; Smoreda, Z.; Plötz, T. Detecting home locations from CDR data: Introducing spatial uncertainty to the state-of-the-art. arXiv, 2018; arXiv:1808.06398. [Google Scholar]
- Cottineau, C. MetaZipf. A dynamic meta-analysis of city size distributions. PLoS ONE
**2017**, 12, e0183919. [Google Scholar] [CrossRef] - Gower, J.C. A general coefficient of similarity and some of its properties. Biometrics
**1971**, 27, 857–871. [Google Scholar] [CrossRef] - Kaufman, L.; Rousseeuw, P. Clustering by Means of Medoids; Faculty of Mathematics and Informatics: Sofia, Bulgaria, 1987. [Google Scholar]
- Cattell, V. Poor people, poor places, and poor health: The mediating role of social networks and social capital. Soc. Sci. Med.
**2001**, 52, 1501–1516. [Google Scholar] [CrossRef] - Granovetter, M. The strength of weak ties: A network theory revisited. Sociol. Theory
**1983**, 1, 201–233. [Google Scholar] [CrossRef] - Eeckhout, J.; Pinheiro, R.; Schmidheiny, K. Spatial sorting. J. Polit. Econ.
**2014**, 122, 554–620. [Google Scholar] [CrossRef] - Sarkar, S. Urban scaling and the geographic concentration of inequalities by city size. Environ. Plan. B Urban Anal. City Sci.
**2018**. [Google Scholar] [CrossRef]

**Figure 1.**Maps of deprivation, inequality and segregation levels for metropolitan areas with more than 10,000 residents in 2011.

**Figure 2.**Maps of several mobile phone indicators at cell tower level. Indicators are computed for September 2007, for all users in the French CDR dataset. Users are aggregated at cell tower level by the the Distinct Days Algorithm. Each dot on the map is a cell tower and displays the average indicator value of all users allocated to this cell tower. Cell towers with an average value that is higher, or lower, than 3 standard deviations from the nationwide average are omitted, hence the differing number of displayed cell towers (n) between maps.

**Figure 4.**Distributions of the Spearman correlation coefficient for the relation between EDI and a selection of mobile phone indicators calculated for all 4914 city definitions. The histogram is colored green when correlation coefficients are positive and orange when negative.

**Figure 5.**Distributions of the Spearman correlation coefficient for the relation between the Gini index and a selection of mobile phone indicators calculated for all 4914 city definitions. The histogram is coloured green when correlation coefficients are positive and orange when negative.

**Figure 6.**Distributions of the Spearman correlation coefficient for the relation between the Segregation index and a selection of mobile phone indicators calculated for all 4914 city definitions. The histogram is colored green when correlation coefficients are positive and orange when negative.

**Figure 7.**Distributions of the Spearman correlation coefficient for the relation between the Entropy of contacts and the different socioeconomic indicators. Correlation coefficients are calculated for all 4914 city definitions but the histograms group results by the different classes of city definitions as defined in Figure 3. The bars in the histograms are coloured green when correlation coefficients are positive and orange when negative. The colours outlining the histograms accord to the different classes as defined in Figure 3.

**Figure 8.**Heatmap of the Pearsons’s R for the relation between the entropy of contacts and the segregation index of wages for all city definitions represented in their according parameter-space. Each square on the heatmaps represents one of the 4914 city definitions projected in the space of definition criteria (x = density threshold, y = flow threshold, z = population threshold). It is coloured according to the value of the correlation index between the variable for the given definition. Density thresholds (d) are for the city centers and in thousands inhabitants/hectare, flow thresholds (f) are in percentage of population commuting to the city center, and population thresholds (p) are in thousands inhabitants in the wider city. As can be deduced, the top row plots have a population threshold (p) of 0.

**Figure 9.**Significant coefficients in a multiple regression of mobile indicators by cluster medoid. NB: In this figure, the number of observation N refers to the number of clusters within the representative medoid definition. It is a number of cities which are included in the regression for a given definition.

Mobile Phone Indicator | Description |
---|---|

Number of calls | Number of calls made or received |

Active days | Number of active distinct days |

Percentage nocturnal calls | Percentage of calls made between 7 p.m. and 9 a.m. |

Duration of calls | Mean duration of all calls |

Inter-event time | Mean duration between consecutive calls |

Number of contacts | Number of contacts interacted with |

Interaction per contact | Mean amount of interactions per contact |

Entropy of contacts * (Equation (3)) | Entropy measure of calls to contacts |

Number of visited cell towers | Number of cell towers used to make calls |

Radius of gyration * (Equation (1)) | Radius of gyration of movement patterns based on visited cell towers |

Entropy of visited cell towers * (Equation (2)) | Entropy measure of visited cell towers |

Distance between l1 and l2 | Distance between most plausible and second most plausible ’home’ cell tower given a home detection algorithm |

Spatial uncertainty | Uncertainty measure of the detection of the most plausible home location |

Calls at home | Number of calls made at the presumed home cell tower |

Percentage calls at home | Percentage of calls made at the presumed home cell tower |

**Table 2.**Spearman correlations between three census indicators for the centroid definition of all the six classes.

Deprivation–Inequality | Inequality–Segregation | Segregation–Deprivation | |
---|---|---|---|

UrbanAreas | 0.060 | −0.082 | −0.028 |

Dispersed | 0.062 | −0.252 | −0.144 |

UrbanCores | −0.044 | 0.192 | 0.186 |

MetroMedium | −0.059 | −0.047 | −0.156 |

MetroCore | −0.072 | 0.119 | −0.131 |

MetroWide | −0.041 | −0.156 | −0.069 |

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**MDPI and ACS Style**

Cottineau, C.; Vanhoof, M.
Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities. *ISPRS Int. J. Geo-Inf.* **2019**, *8*, 19.
https://doi.org/10.3390/ijgi8010019

**AMA Style**

Cottineau C, Vanhoof M.
Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities. *ISPRS International Journal of Geo-Information*. 2019; 8(1):19.
https://doi.org/10.3390/ijgi8010019

**Chicago/Turabian Style**

Cottineau, Clémentine, and Maarten Vanhoof.
2019. "Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities" *ISPRS International Journal of Geo-Information* 8, no. 1: 19.
https://doi.org/10.3390/ijgi8010019