An end-to-end statistical process with mobile network data for official statistics

Abstract

Mobile network data has been proven to provide a rich source of information in multiple statistical domains such as demography, tourism, urban planning, etc. However, the incorporation of this data source to the routinely production of official statistics is taking many efforts since a diversity of highly entangled issues (access, methodology, IT tools, quality, skills) must be solved beforehand. To do this, one-off studies with concrete data sets are not enough and a standard statistical production process must be put in place. We propose a concrete modular process structured into evolvable modules detaching the strongly technological layer underlying this data source from the necessary statistical analysis producing outputs of interest. This architecture follows the principles of the so-called ESS Reference Methodological Framework for Mobile Network Data. Each of these modules deals with a different aspect of this data source. We apply hidden Markov models for the geolocation of mobile devices, use a Bayesian approach on this model to disambiguate devices belonging to the same individual, compute aggregate numbers of individuals detected by a telecommunication network using probability theory, and model hierarchically the integration of auxiliary information from the telco market and official data to produce final estimates of the number of individuals across different territorial regions in the target population. A first simple illustrative proposal has been applied to synthetic data providing preliminary software tools and accuracy indicators monitoring the performance of the process. Currently, this exercise has been applied to the estimation of present population and origin-destination matrices. We present an illustrative example of the execution of these production modules comparing results with the simulated ground truth, thus assessing the performance of each production module.