Monthly air passenger flows data are synthetised into a set of indicators between countries worldwide describing trends and types of human cross-border mobility.
Two indicators describe the trend of mobility between countries (for detailed information about the indicators see reference paper below):
- Trend - Spearman’s rank correlation. The first indicator concerns the trend of passengers between countries. The trend is obtained through the time series decomposition of the data and is released at annual granularity for the period 2011 to 2016. The Spearman’s rank correlation provides information on the monotonicity of the increase or decrease in the trend over time.
- Trend intensity - Difference between 2016 and 2011 trend value. The second measure indicates the intensity of the change in mobility between the first and last year under study; based on the annual trend time series. This indicator was normalized with respect to the population of the countries of origin.
The other two indicators describe the seasonal pattern of mobility between countries:
- Lag. For each pair of countries; the cross-correlation of seasonal mobility in both directions was calculated. The lag indicates the asynchronicity of seasonal flows between the country pair. It allows to understand the type of mobility that dominates seasonal mobility between countries; with a lag = 0 suggesting mass tourism and a lag > 0 indicating long-distance individual tourism or seasonal work migration.
- Prominence - Peak ratio. The last indicator is the ratio between the number of people moving during the peak month of the seasonal component and the mean overall number of passengers per year. It describes the prominence of the seasonal mobility relative to all mobility between a given country pair.
The work was jointly carried out by the European Commission Knowledge Centre on Migration and Demography (KCMD) and the European University Institute (EUI) Migration Policy Centre (MPC) as part of the Global Mobilities Project (GMP).
How to access the data: The data can be downloaded from the Dynamic Data Hub by selecting the relevant dataset and pressing F4.
How to cite: L. Gabrielli; E. Deutschmann; F. Natale; E. Recchi and M. Vespe. Dissecting global air traffic data to discern different types and trends of transnational human mobility. EPJ Data Science 8; Article number: 26(2019). DOI: 10.1140/epjds/s13688-019-0204-x
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