DOI: 10.7910/dvn/ae1pkc
title: Air Traffic Data International Mobility Indicators for the UK

description: The Air Traffic Data International Mobility Indicators for the UK results from the investigation on air passenger data from the Sabre Corporation [1], accessed through a collaboration with the JRC Ispra. Starting from air passenger traffic volumes from each country of origin and to the final country of destination, two mobility indicators based on log flow ratios were provided: the Flow Log Ratio (FLR) and the Cumulative Flow Log Ratio (CFLR). These indicators, computed with monthly and yearly resolution, allow to eliminate short term trips observing the general pattern of longer-term mobility. 

The Flow Log Ratio (FLR) is defined as the logarithm of the ratio between the number of incoming individuals in a given country (e.g., entering the UK) and the number of outgoing individuals in the same observed country (e.g., leaving the UK). Specifically, for each country or set of countries of origin and destination (C1, C2), and over a specified period of time, t, we consider the incoming flow FI(t) (from C2 to C1) and the outgoing flow FO(t) (from C1 to C2). The Flow Log Ratio FLR(t) is then defined as log2(FI(t)/FO(t)). 
If the FLR is below 0, it means that more individuals moved out of C1 compared to those who moved in, while an index above 0 shows that C1 is an attractive country with more people coming in. An FLR of 1 means the incoming flows are twice as large as outgoing flows, while an FLR of -1 means the outgoing flows are twice less. The FLR is an indicator that allows to study the trends point by point in time and observe point-wise changes in trends. 

The Cumulative Flow Log Ratio (CFLR) is defined as the logarithm of the ratio between the cumulative incoming flows and cumulative outgoing flows up to the current time window t. Compared to the FLR, the CFLR allows to evaluate cumulative pattens over much longer periods, rather than performing a point-wise analysis. 

The indicators are provided for the UK versus the rest of the European Union. Further, we provide regional indicators using the division of EU member states into regions proposed by the EuroVoc vocabulary [2]: Northern (Finland, Denmark, Sweden, Estonia, Latvia, Lithuania), Southern (Greece, Italy, Malta, Portugal, Cyprus, Spain), Western (France, Germany, Ireland, Luxembourg, Netherlands, Austria, Belgium), Central and Eastern (Hungary, Poland, Romania, Bulgaria, Croatia, Slovakia, Czechia, Slovenia). Europe-level indicators are also included. 

The entire Air Traffic Data International Mobility Indicators for the UK includes monthly and yearly Flow Log Ratio and Cumulative Flow Log Ratio indicators calculated at different spatial and time resolutions. Further, the monthly set also provides the components obtained by applying Seasonal-Trend decomposition (TSD) [3] to FLR regional values. These allow for separating seasonal from overall patterns. 

The Air Traffic Data International Mobility Indicators for the UK include FLRs and CFLRs values calculated for the United Kingdom versus a) the 27 countries in the European Union, b) the four regions of the European Union, and c) the entire European Union. Monthly data are provided from February 2011 to October 2021, while yearly data covers 2011-2021. Moreover, the monthly dataset includes components, i.e., trend, seasonal, and residual signals, obtained by decomposing the regional EU FLRs with Statsmodels [4] Python library (using an additive model with 12 components). 

In publishing the dataset, we followed the DEU guidelines for publishing high-quality data. To ensure interoperability and facilitate automatic processing by machines, we used the CSV format with US-ASCII encoding. All country names follow the ISO2 standard. The European subregions follow the EuroVoc vocabulary, dates are standardised, time series are complete. The CSV files are accompanied by a README that defines all variables included in the data and cross-references publications. 

References:
[1] Sabre. Market intelligence, global demand data. https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=202302101437200109948&URLID=11&ESV=10.0.19.7431&IV=259BC11764855306985B70AF21AF9795&TT=1676039840964&ESN=Vs8xERNXlu7bOs3Tyb9f%2Fa8tNspLAa%2FGwagIu4vHdcQ%3D&KV=1536961729280&B64_ENCODED_URL=aHR0cHM6Ly93d3cuc2FicmUuY29tL3Byb2R1Y3RzL21hcmtldC1pbnRlbGxpZ2VuY2UvLA&HK=D2BCC95C29FB56BEC2A395CC3D9C17C53D482CA86C9C38AA591FB4CEC3FD597F 2021. Accessed: 2021-11-15. 

[2] https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=202302101437200109948&URLID=10&ESV=10.0.19.7431&IV=2934525D891132A3AEF7FAE3284ABBF5&TT=1676039840964&ESN=1y%2BYp5gdrdyZM9uJx0B%2FPBEP1rDDsKvDHe7LgSX0cS8%3D&KV=1536961729280&B64_ENCODED_URL=aHR0cHM6Ly9ldXItbGV4LmV1cm9wYS5ldS9icm93c2UvZXVyb3ZvYy5odG1sP3BhcmFtcz03Miw3MjA2&HK=8C84248906662B84FF5949BF9C969AA3FE97AB3970282A47E9BDFA1EB8E0B1F6 

[3] Cleveland, R.B., Cleveland, W.S., McRae, J.E. and Terpenning, I., 1990. STL: A seasonal-trend decomposition. J. Off. Stat, 6(1), pp.3-73. 
[4] McKinney, W., Perktold, J., & Seabold, S. (2011). Time series analysis in python with statsmodels. Jarrodmillman. Com, 96-102.