Abstract
Beginning with the results of a research carried out in the Italian region of Lombardy utilising mobile phone data provided by Telecom Italia, this chapter will demonstrate how new maps , based on mobile phone data and better tailored to the dynamic processes taking place, can represent spatialized urban practices and origin-destination flows of daily movements. Three different types of mobile phone data were employed in the analysis of complex temporal and spatial patterns. The first data type concerns the mobile phone traffic registered by the network over the entire Lombardy Region (Northern Italy). Data are expressed in Erlang, a measure of the density of calls. The second typology of data consists in localized and aggregated tracks of anonymized mobile phone users . It is an origin-destination datum derived from the Call Detail Record database. The third type of data refers to the mobile switching centre (MSC), which is the primary service delivery node for GSM, responsible for routing voice calls and text messages. With the maps based on the processing of the three types of mobile phone data, it was possible to offer information on temporary populations and city usage patterns (daily/nightly practices, non-systematic mobility).
Fabio Manfredini is the author of Sects. 3.2.4, 3.4; Paola Pucci is the author of Sect. 3.2.3; Paolo Tagliolato is the author of Sects. 3.2.1, 3.3. Sections 3.1, 3.2.2 were written by the three authors.
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Notes
- 1.
More formally we can define the Erlang Exj relevant to the pixel x and to the j-th quarter of an hour as:
$${{\text{E}}_{xj} = \frac{1}{15} {\int_{{{15}\left( {j - 1} \right)}}^{15j}}} {{N}_{x}\left( t \right)dt}$$where Nx(x) is the number of mobile phones using the network within pixel x at time t, hence Exj is the temporal mean over the j-th quarter of an hour of the number of mobile phones using the network within pixel x.
- 2.
Erlang raw data describe the quantity of absolute mobile phone traffic in one pixel at a certain moment.
- 3.
These data do not allow us to draw a direct correlation between the density of phone calls (Erlang data) and the number of people present in a cell, also because, as is well-known, cellular telephone use is conditioned by socio-professional user profiles (age, sex, profession).
- 4.
By mobile phone activity we mean each interaction of the device with the mobile phone network (i.e. calls received or made, SMSs sent or received, internet connections, etc.).
- 5.
Including O/D flow matrices from the Regione Lombardia (2002) and a qualitative survey by the Provincia di Milano (2006), together with land use maps and demographic census data.
- 6.
We considered once again the ratio between the Erlang measures per pixel of each municipality and that of the entire Lombardy Region.
- 7.
In each map we added infrastructures (railways and main roads), railway stations, Linate city airport, main shopping centres and the fair trade centre.
- 8.
These stations, located in Milan, play different roles related to their transport supply: regional stations for commuter flows such as Porta Genova, Bovisa, Affori, Rogoredo, Cadorna, Greco-Bicocca and nodes of national relations (Milano Centrale and Porta Garibaldi).
- 9.
The Wednesday peak in the Central Station from 5 a.m. to 7 a.m. can be explained with the corresponding low value of the denominator of the ratio station Erlang/Lombardy Erlang. When the total amount of telephone traffic is low (0 a.m.–6 a.m.), Erlang Lombardy curves are not very reliable.
- 10.
During the day, the people generally move to the workplace or school, to come back in different times, according to age.
- 11.
The socio-economic data for comparison with Erlang data, are: inhabitants by sex and age; socio-professional profile of population; foreign population; commuter flows; employees and economical activities.
- 12.
With comparison of trends between 7.00 a.m. and 7.00 p.m. on a representative weekday correspondence can be observed between the datum of the number of students enrolled as well as staff in each university and school structure and the intensity of telephone traffic: the “Università Statale di Milano”, which shows the greatest density of telephone traffic, is also the structure which concentrates the greatest quantity of students and staff. Città studi-Leonardo, albeit showing a number of students enrolled and staff only slightly higher than Bicocca University, displays a higher density of telephone traffic in Erlang. This trend could be associated with the significantly higher proportion of students enrolled at Città studi-Leonardo who are more likely to use cell phones than the staff, who have access to landline telephones in the workplace.
- 13.
The indicator is calculated as: employees + resident population − commuter outflows.
- 14.
The 49th edition of the “Salone Internazionale del Mobile”, which lasted from 14 to 19 April 2010 at the Rho-Pero exhibition district, has attracted 298,000 visitors, including 166,000 foreigners (Source www.cosmit.it).
- 15.
The present work could not take into account the last census data on commuter mobility, relative to the year 2011, being it published in August 2014 after the preparation of this book.
- 16.
- 17.
The 2010 edition registered about 300,000 visitors, more than half being foreigners (Cosmit).
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Pucci, P., Manfredini, F., Tagliolato, P. (2015). Daily Mobility Practices Through Mobile Phone Data: An Application in Lombardy Region. In: Mapping Urban Practices Through Mobile Phone Data. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-14833-5_3
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