Treelet decomposition of mobile phone data for deriving city usage and mobility pattern in the Milan urban region
Thursday 17th May 2012
Manfredini, F.; Pucci, P.; Secchi, P.; Tagliolato, P.; Vantini, S.; Vitelli, V.
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 dierent 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 specic eect 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 signicant for explaining specic mobility and city usages patterns (commuting, nightly activities, distribution of residences, non systematic mobility) and tested their signicance and their interpretation from an urban analysis and planning perspective at the Milan urban region scale.