Using Vodafone mobile phone network data to provide insights into citizens mobility in Italy during the Coronavirus outbreak

Abstract

In this paper, we present the work conducted by Vodafone to enrich the understanding of people movement in Italy during the outbreak of the Coronavirus in 2020, and the tool developed to support the decisions taken by the authorities during that period. We have developed a solution to anonymously monitor the daily movements of Vodafone SIMs in Italy, at aggregate level, at different spatial and temporal granularity, to provide insights into the movements of Italians.

Spatio-temporal distribution pattern of COVID-19 in the Northern Italy during the first-wave scenario: The role of the highway network

“…1. the starting day of the analysis was set as the February 27. The time observation was set according to the space–time progression of the measures for the containment and management of the epidemiological emergency by COVID-19 adopted in Italy ( Collivignarelli et al, 2020 ) from February 23 (11 municipalities in the Northern Italy were identified as the center of the two main Italian clusters and placed under quarantine) ( Boccia et al, 2020 , Calabrese et al, 2021 ) to March 10 (lockdown was extended to the whole territory) ( Mishra et al, 2020 );…”

Mobile Big Data in the fight against COVID-19

“…The authors bring these principles to life through discussions of specific projects in Estonia, the Gambia, and Ghana. Calabrese et al (2021) present the work conducted by Vodafone to enrich the understanding of people movement in Italy during the outbreak of the Coronavirus in 2020, and the tool developed to support the decisions taken by the authorities during that period. Vodafone have developed a solution to anonymously monitor the daily movements of Vodafone SIMs in Italy, at aggregate level, at different spatial and temporal granularity, to provide insights into the movements of Italians.…”

MAT-Builder: a System to Build Semantically Enriched Trajectories

“…Generally speaking, this process requires to identify (1) the parts of the trajectory to be enriched, (2) the various types and sources of semantic data to be used for the enrichment, and (3) the most suitable approaches to properly associate spatio-temporal data with semantic information. For what concerns existing solutions, there are several libraries [3], [4] (e.g., Geopandas 1 ), dashboards (e.g., [5], [6]), and ontology-based approaches (e.g., [7]) that are able to process and extract insights from trajectory data, while others propose trajectory enrichment with specific data sources (e.g., [8]). Unfortunately, these solutions either do not perform semantic enrichment or, when they do, they are limited to a fixed number of aspects, are not extensible, or they do not support the use of external data sources.…”