Abstract
The paper presents a novel geo-statistical unsupervised learning technique aimed at identifying useful information on hidden patterns of mobile phone use. These hidden patterns regard different usages of the city in time and in space which are related to individual mobility, outlining the potential of this technology for the urban planning community. The methodology allows to obtain a reference basis that reports the specific effect of some activities on the Erlang data recorded and a set of maps showing the contribution of each activity to the local Erlang signal. We selected some results as significant for explaining specific mobility and city usages patterns (commuting, nightly activities, distribution of residences, non systematic mobility) and tested their significance and their interpretation from an urban analysis and planning perspective at the Milan urban region scale.
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Acknowledgements
The authors would like to acknowledge Piero Lovisolo, Dario Parata and Massimo Colonna, Tilab—Telecom Italia for their collaboration during the research project. This work was supported by Telecom Italia. We also thank Paolo Dilda for helping us in the preparation of the figures.
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Manfredini, F., Pucci, P., Secchi, P., Tagliolato, P., Vantini, S., Vitelli, V. (2015). Treelet Decomposition of Mobile Phone Data for Deriving City Usage and Mobility Pattern in the Milan Urban Region. In: Paganoni, A., Secchi, P. (eds) Advances in Complex Data Modeling and Computational Methods in Statistics. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-11149-0_9
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DOI: https://doi.org/10.1007/978-3-319-11149-0_9
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