In the context of the ongoing COVID-19 pandemic, various strategies have been applied by governments around the world to try and reduce its spread. This work focuses on the impact of COVID-19 in relation to human mobility, based on data provided by Google and Apple, analyzing how much the mobility has changed due to the disease and the restrictions imposed. The available data have been obtained from Google Maps or Apple Maps users searches for directions to reach certain categories of places (Google Maps) or a certain place with a specific mean of transportation (Apple Maps). We use Italy as a case study, considering data from January 2020 to December of 2021. First an archive was created to collect all these data for offline use. Then, we statistically analyzed the dataset focusing on two different spatial scales, national and regional. For the latter, we selected Lombardy and Veneto, two regions that were critically hit in the first phase of the pandemic and that, together, account for about one quarter of the total Italian population. The analysis showed that mobility changes were quite similar at national and regional levels, presenting very similar mobility profiles. To assess similarities and differences between mobility categories and for means of transportation, we mainly used ANOVA, multiple comparisons, Kolmogorov-Smirnov test, and linear regression. As simple linear models were not always sufficient to explain the correlation patterns of different mobility signals, alternative model formulations were tested and evaluated using the Akaike Information Criterion. The results presented in thesis may help inform spatially epidemiological models explicit with a much-needed data-based description of human mobility.
Nel contesto dell'attuale pandemia di COVID-19, i governi di tutto il mondo hanno applicato varie strategie per cercare di ridurne la diffusione. Questo lavoro si concentra sull'impatto del COVID-19 in relazione alla mobilità umana, sulla base dei dati forniti da Google e Apple, analizzando quanto è cambiata la mobilità a causa della malattia e le restrizioni imposte. I dati disponibili sono stati ottenuti dagli utenti di Google Maps o di Apple Maps che cercano indicazioni stradali per raggiungere determinate categorie di luoghi (Google Maps) o un determinato luogo con uno specifico mezzo di trasporto (Apple Maps). Usiamo l'Italia come caso di studio, considerando i dati da gennaio 2020 a dicembre 2021. Innanzitutto, è stato creato un archivio per raccogliere tutti questi dati per l'uso offline. Quindi, abbiamo analizzato statisticamente il set di dati concentrandoci su due diverse scale spaziali, nazionale e regionale. Per quest'ultima abbiamo selezionato Lombardia e Veneto, due regioni che sono state gravemente colpite nella prima fase della pandemia e che, insieme, rappresentano circa un quarto della popolazione italiana totale. L'analisi ha mostrato che i cambiamenti della mobilità erano abbastanza simili a livello nazionale e regionale, presentando profili di mobilità molto affini. Per valutare somiglianze e differenze tra le categorie di mobilità e per i mezzi di trasporto, abbiamo utilizzato principalmente ANOVA, confronti multipli, test di Kolmogorov-Smirnov e regressione lineare. Poiché i semplici modelli lineari non erano sempre sufficienti per spiegare i pattern di correlazione di diversi segnali di mobilità, le formulazioni di modelli alternativi sono state testate e valutate utilizzando il Criterio di Informazione di Akaike. I risultati presentati nella tesi possono aiutare a informare modelli epidemiologici spazialmente esplicitando la forte necessità di avere una base di dati della mobilità umana.
Human movement in pandemic times : the impact of COVID-19 in Italy analyzed through the lens of Google and Apple mobility data
CORBETTA, MATTEO
2020/2021
Abstract
In the context of the ongoing COVID-19 pandemic, various strategies have been applied by governments around the world to try and reduce its spread. This work focuses on the impact of COVID-19 in relation to human mobility, based on data provided by Google and Apple, analyzing how much the mobility has changed due to the disease and the restrictions imposed. The available data have been obtained from Google Maps or Apple Maps users searches for directions to reach certain categories of places (Google Maps) or a certain place with a specific mean of transportation (Apple Maps). We use Italy as a case study, considering data from January 2020 to December of 2021. First an archive was created to collect all these data for offline use. Then, we statistically analyzed the dataset focusing on two different spatial scales, national and regional. For the latter, we selected Lombardy and Veneto, two regions that were critically hit in the first phase of the pandemic and that, together, account for about one quarter of the total Italian population. The analysis showed that mobility changes were quite similar at national and regional levels, presenting very similar mobility profiles. To assess similarities and differences between mobility categories and for means of transportation, we mainly used ANOVA, multiple comparisons, Kolmogorov-Smirnov test, and linear regression. As simple linear models were not always sufficient to explain the correlation patterns of different mobility signals, alternative model formulations were tested and evaluated using the Akaike Information Criterion. The results presented in thesis may help inform spatially epidemiological models explicit with a much-needed data-based description of human mobility.| File | Dimensione | Formato | |
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2022_04_Corbetta.pdf
Open Access dal 12/04/2023
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https://hdl.handle.net/10589/186877