A glimpse into mobile phone data: characteristics, organization, tools
Abstract
This paper aims to present the presence and mobility data provided by TIM, highlighting the acquisition methodology, the levels of spatial and temporal disaggregation, as well as the additional information related to age groups, gender, and classification of behaviours, which are directly supplied by TIM. The construction of a baseline based on mobile phone data for the comparison of temporal trends in the presence of people is also discussed.
At the same time, the supporting data obtained from traditional sources or ad hoc surveys will be presented to show how they can facilitate the interpretation of telephone data, its validation, and its use. Finally, a reference on the operational tools used for their processing and visualization will highlight the need to integrate skills, methodologies, and tools for the maximum exploitation of this wealth of information.
Downloads
References
AGCOM (2022) Osservatorio sulle comunicazioni n. 4/2021
Ahas, R., Aasa, A., Roose, A., Mark, Ü., & Silm, S. (2008). Evaluating passive mobile positioning data for tourism surveys: An Estonian case study. Tourism Management, 29(3), 469-486. https://doi.org/10.1016/j.tourman.2007.05.014
Barcaroli G, De Francisci S, Scannapieco M, Summa D (2014) Dealing with big data for official statistics: IT issues. Meeting on the management of statistical information systems, Dublin, Ireland and Manila, Philippines, pp 14–16
Blondel, V. D., Decuyper, A., & Krings, G. (2015). A survey of results on mobile phone datasets analysis. EPJ data science, 4(1), 10. https://doi.org/10.1140/epjds/s13688-015-0046-0
Curci, F., Lanza, G. & Manfredini, F. (2022). Mobile phone traffic data for territorial research. Opportunities and challenges for urban sensing and territorial fragilities analysis. Tema. Journal of Land Use, Mobility and Environment. http://10.6092/1970-9870/8892
Curci, F., Kercuku, A., Zanfi, F. & Novak, C. (2022). Permanent and Seasonal Human Presence in the Coastal Settlements of Lecce. An Analysis Using Mobile Phone Tracking Data. Tema. Journal of Land Use, Mobility and Environment. http://10.6092/1970-9870/8914
Daas, P. J., Puts, M. J., Buelens, B., & van den Hurk, P. A. (2015). Big data as a source for official statistics. Journal of Official Statistics, 31(2), 249. http://dx.doi.org/10.1515/JOS-2015-0016
De Montjoye, Y. A., Gambs, S., Blondel, V., Canright, G., De Cordes, N., Deletaille, S., ... & Bengtsson, L. (2018). On the privacy-conscientious use of mobile phone data. Scientific data, 5(1), 1-6. https://doi.org/10.1038/sdata.2018.286
Dobra, A., Williams, N. E., & Eagle, N. (2015). Spatiotemporal detection of unusual human population behavior using mobile phone data. PloS one, 10(3), e0120449. https://doi.org/10.1371/journal.pone.0120449
Gething, P. W., & Tatem, A. J. (2011). Can mobile phone data improve emergency response to natural disasters?. PLoS medicine, 8(8), e1001085. https://doi.org/10.1371/journal.pmed.1001085
Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L. (2008). Understanding individual human mobility patterns. nature, 453(7196), 779-782. https://doi.org/10.1038/nature06958
Lanza, G., Pucci, P., Vendemmia, B. & Carboni, L. (2022). Impacts of the Covid 19 outbreak on two Apennine valleys. Remote-working and near-home tourism through mobile phone data. Tema. Journal of Land Use, Mobility and Environment. http://10.6092/1970-9870/8915
Louail, T., Lenormand, M., Cantu Ros, O. G., Picornell, M., Herranz, R., Frias-Martinez, E., ... & Barthelemy, M. (2014). From mobile phone data to the spatial structure of cities. Scientific reports, 4 (1), 1-12. https://doi.org/10.1038/srep05276
Maas, P., Iyer, S., Gros, A., Park, W., McGorman, L., Nayak, C., & Dow, P. A. (2019, May). Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery. In KDD (Vol. 19, p. 3173). Shmelev, S.E. (2019). Sustainable cities reimagined: multidimensional assessment and smart solutions. New York: Routledge.
Manfredini, F., Pucci, P., & Tagliolato, P. (2013). Mobile phone network data: new sources for urban studies?. In Geographic information analysis for sustainable development and economic planning: New technologies, 115-128. IGI Global. https://doi.org/10.4018/978-1-4666-1924-1.ch008
Manfredini, F., Tagliolato, P., & Rosa, C. D. (2011, June). Monitoring temporary populations through cellular core network data. In International Conference on Computational Science and Its Applications, 151-161. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21887-3_12
Mariotti, I., Giavarini, V., Rossi, F. & Akhavan, M. (2022). Exploring the “15-Minute City” and near working in Milan using mobile phone data. Tema. Journal of Land Use, Mobility and Environment. http://10.6092/1970-9870/9309
Pucci, P., Manfredini, F., & Tagliolato, P. (2015). Mapping urban practices through mobile phone data (Vol. 3, No. 4). Heidelberg, New York, Dordrecht, London: Springer. https://doi.org/10.1007/978-3-319-14833-5
Soto, V., & Frías-Martínez, E. (2011, June). Automated land use identification using cell-phone records. In Proceedings of the 3rd ACM international workshop on MobiArch (pp. 17-22). https://doi.org/10.1145/2000172.2000179
Steenbruggen, J., Tranos, E., & Nijkamp, P. (2015). Data from mobile phone operators: A tool for smarter cities?. Telecommunications Policy, 39 (3-4), 335-346. https://doi.org/10.1016/j.telpol.2014.04.001
Struijs, P., Braaksma, B., & Daas, P. J. (2014). Official statistics and big data. Big Data & Society, 1(1), 2053951714538417. https://doi.org/10.1177/2053951714538417
Toole, J. L., Ulm, M., González, M. C., & Bauer, D. (2012, August). Inferring land use from mobile phone activity. In Proceedings of the ACM SIGKDD international workshop on urban computing, 1-8. https://doi.org/10.1145/2346496.2346498
Vanhoof, M., Reis, F., Ploetz, T., & Smoreda, Z. (2018). Assessing the quality of home detection from mobile phone data for official statistics. arXiv preprint arXiv:1809.07567. https://doi.org/10.48550/arXiv.1809.07567
Wang, Z., He, S. Y., & Leung, Y. (2018). Applying mobile phone data to travel behaviour research: A literature review. Travel Behaviour and Society, 11, 141-155. https://doi.org/10.1016/j.tbs.2017.02.005
Copyright (c) 2022 TeMA - Journal of Land Use, Mobility and Environment

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish in this journal agree to the following:
1. Authors retain the rights to their work and give in to the journal the right of first publication of the work simultaneously licensed under a Creative Commons License - Attribution that allows others to share the work indicating the authorship and the initial publication in this journal.
2. Authors can adhere to other agreements of non-exclusive license for the distribution of the published version of the work (ex. To deposit it in an institutional repository or to publish it in a monography), provided to indicate that the document was first published in this journal.
3. Authors can distribute their work online (ex. In institutional repositories or in their website) prior to and during the submission process, as it can lead to productive exchanges and it can increase the quotations of the published work (See The Effect of Open Access)