Dissecting global air traffic data to discern different types and trends of transnational human mobility

Abstract

Human mobility across national borders is a key phenomenon of our time. At the global scale, however, we still know relatively little about the structure and nature of such transnational movements. This study uses a large dataset on monthly air passenger traffic between 239 countries worldwide from 2010 to 2018 to gain new insights into (a) mobility trends over time and (b) types of mobility. A time series decomposition is used to extract a trend and a seasonal component. The trend component permits-at a higher level of granularity than previous sources-to examine the development of mobility between countries and to test how it is affected by policy and infrastructural changes, economic developments, and violent conflict. The seasonal component allows, by measuring the lag between initial and return motion, to discern different types of mobility, from tourism to seasonal work migration. Moreover, the exact shape of seasonal mobility patterns is extracted, allowing to identify regular mobility peaks and nadirs throughout the year. The result is a unique classification of trends and types of mobility for a global set of country pairs. A range of implications and possible applications are discussed.

Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States

“…As a consequence of data availability issues, we need to consider how nontraditional data can be leveraged to complement existing sources in order to improve estimates and predictions of migration indicators over time. Previous work has explored the use of data such as call detail records (Blumenstock 2012;Pestre et al 2020), air traffic data (Gabrielli et al 2019), tax file records (Engels and Healy 1981) and other sources like billing addresses or school enrollment (Foulkes and Newbold 2008) to estimate migration. Additionally, an increasingly large body of work has investigated the use of social media data, from websites such as Twitter (Zagheni et al 2014), Facebook (Zagheni et al 2017) and LinkedIn (State et al 2014).…”

Estimating Transnational Human Mobility on a Global Scale

“…Here, we draw on a simplified and reduced version created by researchers at the European Commission's Knowledge Centre for Migration and Democracy (KCMD) that represents the yearly trend between countries (henceforth KCMD-revised air passenger trend data [2]). This version was generated through a time-series decomposition that dissects the raw overall air passenger flow between two countries into a trend component, a seasonal component, and a residual component (Gabrielli et al 2019). In the KCMD-revised air passenger trend data [2] used here, the monthly trend data is aggregated to yearly averages.…”

“…When we lack [3], we take [1]; and vice versa. As final steps, we: -Round decimals (non-integer estimates can occur due to the time-series decomposition applied by Gabrielli et al [2019] and the correction factor introduced in section 3.3). -Add missing full country names and information on the world region a country is situated in based on the United Nations classification (drawing on Duncalfe [2018]).…”

The accelerated infectious disease risk in the Anthropocene: more outbreaks and wider global spread

“…The increasing connectivity of human populations due to international trade and travel (Guimerà et al, 2005;Colizza et al, 2006;Brockmann and Helbing, 2013;Gabrielli et al, 2019), the rapid growth of the transport of wild and domesticated animals worldwide (Rosen and Smith, 2010;Schneider, 2012;Rohr et al, 2019;Levitt, 2020), and other factors such as the increasing encroachment of human populations on hitherto isolated wild animal populations through loss and fragmentation of wild habitats (Patz et al, 2004;Despommier et al, 2006;Pongsiri et al, 2009;Myers et al, 2013) have led to a great acceleration of infectious disease risks, e.g., the increase in emerging infectious diseases and drug-resistant microbes since 1940 (Jones et al, 2008) and the increase in the number of disease outbreaks since 1980 (Smith et al, 2014).…”