PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Furletti B., Gabrielli L., Giannotti F., Milli L., Nanni M., Pedreschi D., Vivio R., Garofalo G. Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach. In: SIS 2014 - 47th Scientific Meeting of the Italian Statistical Society (Cagliari, Italy, 11-13 June 2014). Atti, article n. 3026. S. Cabras, T. Di Battista, W. Racugno (eds.). SIS, 2014.
 
 
Abstract
(English)
The Big Data, originating from the digital breadcrumbs of human activities, sensed as a by-product of the technologies that we use for our daily ctivities, let us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data "proxies", as the mobile calls data for mobility. In this paper we investigate to what extent such "big data",in integration with administrative ones, could be a support in producing reliable and timely estimates of inter-city mobility. The study has been jointly developed by Istat, CNR, University of Pisa in the range of interest of the "Commssione di studio avente il compito di orientare le scelte dellIstat sul tema dei Big Data ". In an ongoing project at ISTAT, called "Persons and Places" - based on an integration of administrative data sources, it has been produced a first release of Origin Destination matrix - at municipality level - assuming that the places of residence and that of work (or study) be the terminal points of usual individual mobility for work or study. The coincidence between the city of residence and that of work (or study) - is considered as a proxy of the absence of intercity mobility for a person (we define him a static resident). The opposite case is considered as a proxy of presence of mobility (the person is a dynamic resident: commuter or embedded). As administrative data do not contain information on frequency of the mobility, the idea is to specify an estimate method, using calling data as support, to define for each municipality the stock of standing residents, embedded city users and daily city users (commuters).
URL: http://www.sis2014.it/proceedings/allpapers/3026.pdf
Subject Data mining
Big Data
Official Statistics
Call Data Record
H.2.8 Database Applications. Data mining
68U99 None of the above, but in this section


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