ArticleScientific reports2021
Socioeconomic differences and persistent segregation of Italian territories during COVID-19 pandemic.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 28 citations in OpenAlex.
- Promoting sustainable human mobility for income segregation mitigation.Patterns (New York, N.Y.) · 2026Article
- Predicting economic resilience of territories in Italy during the COVID-19 first lockdown.Expert systems with applications · 2023Article
- COVID-19 is linked to changes in the time-space dimension of human mobility.Nature human behaviour · 2023Article
- Multiscale mobility patterns and the restriction of human movement.Royal Society open science · 2023Article
- Metrics for network comparison using egonet feature distributions.Scientific reports · 2023Article
- One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study.Journal of medical Internet research · 2023Article
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lockdowns implemented to address the COVID-19 pandemic have disrupted human mobility flows around the globe to an unprecedented extent and with economic consequences which are unevenly distributed across territories, firms and individuals. Here we study socioeconomic determinants of mobility disruption during both the lockdown and the recovery phases in Italy. For this purpose, we analyze a massive data set on Italian mobility from February to October 2020 and we combine it with detailed data on pre-existing local socioeconomic features of Italian administrative units. Using a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters. Notably, the former cluster features significantly higher income per capita and lower income inequality than the latter. This distinction persists once the lockdown is lifted. The least affected areas display a swift (V-shaped) recovery in mobility patterns, while poorer, most affected areas experience a much slower (U-shaped) recovery: as of October 2020, their mobility was still significantly lower than pre-lockdown levels. These results are then detailed and confirmed with a quantile regression analysis. Our findings show that economic segregation has, thus, strengthened during the pandemic.
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