When COVID-19 first struck the provinces of Northern Italy in early 2020 (especially in Lombardy and in EmiliaRomagna), the conditions there made it a perfect storm. The virus outbreak spread with an unusual violence (in the period from late February to April 2020), with a catastrophic toll in terms of human deaths. Taken by surprise, Italy mandated a complete nation-wide lockdown, successively resorting to ministerial decrees alleviating and postponing the restrictions. Now more than ever, there is an increased awareness on ICT used to combat the pandemic. In this article, we present a quantitative analysis evidencing the impact of restrictions on mobility. To this end, we rely on a vehicular mobility dataset confined in the downtown area of Bologna, Italy. Pursuing the objective, we propose a modified version of a state-of-theart data mining algorithm, allowing us to efficiently identify and quantify mobility flows. The proposal, if combined with additional data sources, could allow for a fine-grained and timely decision making, combating the pandemic.

Measuring the Impact of COVID-19 Restrictions on Mobility: A Real Case Study from Italy

Cavallaro, C
Primo
;
Di Modica, G;
2021

Abstract

When COVID-19 first struck the provinces of Northern Italy in early 2020 (especially in Lombardy and in EmiliaRomagna), the conditions there made it a perfect storm. The virus outbreak spread with an unusual violence (in the period from late February to April 2020), with a catastrophic toll in terms of human deaths. Taken by surprise, Italy mandated a complete nation-wide lockdown, successively resorting to ministerial decrees alleviating and postponing the restrictions. Now more than ever, there is an increased awareness on ICT used to combat the pandemic. In this article, we present a quantitative analysis evidencing the impact of restrictions on mobility. To this end, we rely on a vehicular mobility dataset confined in the downtown area of Bologna, Italy. Pursuing the objective, we propose a modified version of a state-of-theart data mining algorithm, allowing us to efficiently identify and quantify mobility flows. The proposal, if combined with additional data sources, could allow for a fine-grained and timely decision making, combating the pandemic.
Big data
COVID-19
pattern mining
vehicular mobility
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/540771
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