Nowcasting tourist nights spent using innovative human mobility data.

The publication of tourism statistics often does not keep up with the highly dynamic tourism demand trends, especially critical during crises. Alternative data sources such as digital traces and web searches represent an important source to potentially fill this gap, since they are generally timely, and available at detailed spatial scale. In this study we explore the potential of human mobility data from the Google Community Mobility Reports to nowcast the number of monthly nights spent at sub-national scale across 11 European countries in 2020, 2021, and the first half of 2022. Using a machine learning implementation, we found that this novel data source is able to predict the tourism demand with high accuracy, and we compare its potential in the tourism domain to web search and mobile phone data. This result paves the way for a more frequent and timely production of tourism statistics by researchers and statistical entities, and their usage to support tourism monitoring and management, although privacy and surveillance concerns still hinder an actual data innovation transition.

Territorial differences in the spread of COVID-19 in European regions and US counties.

This article explores the territorial differences in the onset and spread of COVID-19 and the excess mortality associated with the pandemic, with a focus on European regions and US counties. Both in Europe and in the US, the pandemic arrived earlier and recorded higher Rt values in urban regions than in intermediate and rural ones. A similar gap is also found in the data on excess mortality. In the weeks during the first phase of the pandemic, urban regions in EU countries experienced excess mortality of up to 68 pp more than rural ones. We show that, during the initial days of the pandemic, territorial differences in Rt by the degree of urbanisation can be largely explained by the level of internal, inbound and outbound mobility. The differences in the spread of COVID-19 by rural-urban typology and the role of mobility are less clear during the second wave. This could be linked to the fact that the infection is widespread across territories, to changes in mobility patterns during the summer period as well as to the different containment measures which reverse the link between mobility and Rt.

The potential of Facebook advertising data for understanding flows of people from Ukraine to the European Union.

This work contributes to the discussion on how innovative data can support a fast crisis response. We use operational data from Facebook to gain useful insights on where people fleeing Ukraine following the Russian invasion are likely to be displaced, focusing on the European Union. In this context, it is extremely important to anticipate where these people are moving so that local and national authorities can better manage challenges related to their reception and integration. By means of the audience estimates provided by Facebook advertising platform, we analyse the flows of people fleeing Ukraine towards the European Union. At the fifth week since the beginning of the war, our results indicate an increase in the number of Ukrainian stocks derived from Ukrainian-speaking Facebook user estimates in all the European Union (EU) countries, with Poland registering the highest percentage share (33%) of the overall increase, followed by Germany (17%), and Czechia (15%). We assess the reliability of prewar Facebook estimates by comparison with official statistics on the Ukrainian diaspora, finding a strong correlation between the two data sources (Pearson's r = 0.9 , p < 0.0001 ). We then compare our results with data on refugees in EU countries bordering Ukraine reported by the UNHCR, and we observe a similarity in their trend. In conclusion, we show how Facebook advertising data could offer timely insights on international mobility during crises, supporting initiatives aimed at providing humanitarian assistance to the displaced people, as well as local and national authorities to better manage their reception and integration.

The impact of air travel on the precocity and severity of COVID-19 deaths in sub-national areas across 45 countries.

Human travel fed the worldwide spread of COVID-19, but it remains unclear whether the volume of incoming air passengers and the centrality of airports in the global airline network made some regions more vulnerable to earlier and higher mortality. We assess whether the precocity and severity of COVID-19 deaths were contingent on these measures of air travel intensity, adjusting for differences in local non-pharmaceutical interventions and pre-pandemic structural characteristics of 502 sub-national areas on five continents in April-October 2020. Ordinary least squares (OLS) models of precocity (i.e., the timing of the 1st and 10th death outbreaks) reveal that neither airport centrality nor the volume of incoming passengers are impactful once we consider pre-pandemic demographic characteristics of the areas. We assess severity (i.e., the weekly death incidence of COVID-19) through the estimation of a generalized linear mixed model, employing a negative binomial link function. Results suggest that COVID-19 death incidence was insensitive to airport centrality, with no substantial changes over time. Higher air passenger volume tends to coincide with more COVID-19 deaths, but this relation weakened as the pandemic proceeded. Different models prove that either the lack of airports in a region or total travel bans did reduce mortality significantly. We conclude that COVID-19 importation through air travel followed a 'travel as spark' principle, whereby the absence of air travel reduced epidemic risk drastically. However, once some travel occurred, its impact on the severity of the pandemic was only in part associated with the number of incoming passengers, and not at all with the position of airports in the global network of airline connections.

Mobility functional areas and COVID-19 spread.

This work introduces a new concept of functional areas called Mobility Functional Areas (MFAs), i.e., the geographic zones highly interconnected according to the analysis of mobile positioning data. The MFAs do not coincide necessarily with administrative borders as they are built observing natural human mobility and, therefore, they can be used to inform, in a bottom-up approach, local transportation, spatial planning, health and economic policies. After presenting the methodology behind the MFAs, this study focuses on the link between the COVID-19 pandemic and the MFAs in Austria. It emerges that the MFAs registered an average number of infections statistically larger than the areas in the rest of the country, suggesting the usefulness of the MFAs in the context of targeted re-escalation policy responses to this health crisis. The MFAs dataset is openly available to other scholars for further analyses.

Anomaly detection of mobile positioning data with applications to COVID-19 situational awareness.

Due to an unprecedented agreement with the European Mobile Network Operators, the Joint Research Centre of the European Commission was in charge of collecting and analyze mobile positioning data to provide scientific evidence to policy makers to face the COVID-19 pandemic. This work introduces a live anomaly detection system for these high-frequency and high-dimensional data collected at European scale. To take into account the different granularity in time and space of the data, the system has been designed to be simple, yet robust to the data diversity, with the aim of detecting abrupt increase of mobility towards specific regions as well as sudden drops of movements. A web application designed for policy makers, makes possible to visualize the anomalies and perceive the effect of containment and lifting measures in terms of their impact on human mobility as well as spot potential new outbreaks related to large gatherings.

Explaining the travelling behaviour of migrants using Facebook audience estimates.

The paper explores the travelling behaviour of migrant groups using Facebook audience estimates. Reduced geographical mobility is associated with increased risk of social exclusion and reduced socio-economic and psychological well-being. Facebook audience estimates are timely, openly available and cover most of the countries in the world. Facebook classifies its users based on multiple attributes such as the country of their previous residence, and whether they are frequent travellers. Using these data, we modelled the travelling behaviour of Facebook users grouped by countries of previous and current residence, gender and age. We found strong indications that the frequency of travelling is lower for Facebook users migrating from low-income countries and for women migrating from or living in countries with high gender inequality. Such mobility inequalities impede the smooth integration of migrants from low-income countries to new destinations and their well-being. Moreover, the reduced mobility of women who have lived or currently live in countries with conservative gender norms capture another aspect of the integration which is referring to socio-cultural norms and gender inequality. However, to provide more solid evidence on whether our findings are also valid for the general population, collaboration with Facebook is required to better understand how the data is being produced and pre-processed.

Measuring the impact of COVID-19 confinement measures on human mobility using mobile positioning data. A European regional analysis.

This work presents a mobility indicator derived from fully anonymised and aggregated mobile positioning data. Even though the indicator does not provide information about the behaviour of individuals, it captures valuable insights into the mobility patterns of the population in the EU and it is expected to inform responses against the COVID-19 pandemic. Spatio-temporal harmonisation is carried out so that the indicator can provide mobility estimates comparable across European countries. The indicators are provided at a high spatial granularity (up to NUTS3). As an application, the indicator is used to study the impact of COVID-19 confinement measure on mobility in Europe. It is found that a large proportion of the change in mobility patterns can be explained by these measures. The paper also presents a comparative analysis between mobility and the infection reproduction number R t over time. These findings will support policymakers in formulating the best data-driven approaches for coming out of confinement, mapping the socio-economic effects of the lockdown measures and building future scenarios in case of new outbreaks.

Estimating and projecting air passenger traffic during the COVID-19 coronavirus outbreak and its socio-economic impact.

Due to the coronavirus global crisis, most countries have put in place restrictive measures in order to confine the pandemia and contain the number of casualties. Among the restrictive measures, air traffic suspension is certainly quite effective in reducing the mobility on the global scale in the short term but it also has high socio-economic impact on the long and short term. The main focus of this study is to collect and prepare data on air passengers traffic worldwide with the scope of analyze the impact of travel ban on the aviation sector. Based on historical data from January 2010 till October 2019, a forecasting model is implemented in order to set a reference baseline. Making use of airplane movements extracted from online flight tracking platforms and on-line booking systems, this study presents also a first assessment of recent changes in flight activity around the world as a result of the COVID-19 pandemic. To study the effects of air travel ban on aviation and in turn its socio-economic, several scenarios are constructed based on past pandemic crisis and the observed flight volumes. It turns out that, according to these hypothetical scenarios, in the first Quarter of 2020 the impact of aviation losses could have negatively reduced World GDP by 0.02% to 0.12% according to the observed data and, in the worst case scenarios, at the end of 2020 the loss could be as high as 1.41-1.67% and job losses may reach the value of 25-30 millions. Focusing on EU27, the GDP loss may amount to 1.66-1.98% by the end of 2020 and the number of job losses from 4.2 to 5 millions in the worst case scenarios. Some countries will be more affected than others in the short run and most European airlines companies will suffer from the travel ban. We hope that these preliminary results may be of help for informed policy making design of exit strategies from this global crisis.

Quantifying international human mobility patterns using Facebook Network data.

Quantifying global international mobility patterns can improve migration governance. Despite decades of calls by the international community to improve international migration statistics, the availability of timely and disaggregated data about long-term and short-term migration at the global level is still very limited. In this study, we investigate the feasibility of using non-traditional data sources to fill existing gaps in migration statistics. To this end, we use anonymised and publicly available data provided by Facebook's advertising platform. Facebook's advertising platform classifies its users as "lived in country X" if they previously lived in country X, and now live in a different country. Drawing on statistics about Facebook Network users (Facebook, Instagram, Messenger, and the Audience Network) who have lived abroad and applying a sample bias correction method, we estimate the number of Facebook Network (FN) "migrants" in 119 countries of residence and in two time periods by age, gender, and country of previous residence. The correction method estimates the probability of a person being a FN user based on age, sex, and country of current and previous residence. We further estimate the correlation between FN-derived migration estimates and reference official migration statistics. By comparing FN-derived migration estimates in two different time periods, January-February and August-September 2018, we successfully capture the increase in Venezuelan migrants in Colombia and Spain in 2018. FN-derived migration estimates cannot replace official migration statistics, as they are not representative, and the exact methods the FN uses for classifying its users are not known, and might change over time. However, after carefully assessing the validity of the FN-derived estimates by comparing them with data from reliable sources, we conclude that these estimates can be used for trend analysis and early-warning purposes.