1,720,988 research outputs found
Air Traffic Data International Mobility Indicators for the UK
No full textThe 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
Use of non-traditional data sources to nowcast migration trends through Artificial Intelligence technologies
No full textIn recent years the pursuit of original drivers and methods is becoming an increasing requirement for migration studies, considering the new technologies used to characterise and understand the human migration phenomenon. In addition to the traditional data typically used in migration studies (e.g., indicators related to the labour market or economic status, measures obtained from surveys and official statistics, either from national censuses or from the population registries), many researchers like Bosco et al. (2022), Fiorio et al. (2017), Gendronneau et al. (2019), Jisu, Sîrbu, Rossetti, Giannotti, and Rapoport (2021), Salah (2021), Spyratos et al. (2018), Sîrbu et al. (2021), Zagheni, Garimella, Weber, and State (2014), Zagheni, Polimis, Alexander, Weber, and Billari (2018), Zagheni, Weber, and Gummadi (2017), have proposed to employ non-traditional data sources to study migration. These can consist in news data, satellite data, but also in digital traces of humans generated by using internet services, mobile phones, IoT devices, fidelity cards, online social networks and many others. This unconventional approach is intended to find an alternative methodology to answer open questions about the human migration framework (i.e., nowcasting flows and stocks, studying the integration of multiple sources and knowledge, and investigating migration drivers). The new data have the advantage of timeliness and large geographical coverage, but also disadvantages in terms of selection bias and amount of resources required to process, as reported by Sîrbu et al. (2021) and Pollacci, Milli, Bircan, and Rossetti (2022). Therefore, models extracted from these data need to be carefully validated, typically with traditional data sources. In this context of meaningful data combination, many types of data exist, still very scattered and heterogeneous, making integration far from straightforward
Use of non-traditional data sources to nowcast migration trends through Artificial Intelligence technologies
No full textIn recent years the pursuit of original drivers and methods is becoming an increasing requirement for migration studies, considering the new technologies used to characterise and understand the human migration phenomenon. In addition to the traditional data typically used in migration studies (e.g., indicators related to the labour market or economic status, measures obtained from surveys and official statistics, either from national censuses or from the population registries), many researchers like Bosco et al. (2022), Fiorio et al. (2017), Gendronneau et al. (2019), Jisu, Sîrbu, Rossetti, Giannotti, and Rapoport (2021), Salah (2021), Spyratos et al. (2018), Sîrbu et al. (2021), Zagheni, Garimella, Weber, and State (2014), Zagheni, Polimis, Alexander, Weber, and Billari (2018), Zagheni, Weber, and Gummadi (2017), have proposed to employ non-traditional data sources to study migration. These can consist in news data, satellite data, but also in digital traces of humans generated by using internet services, mobile phones, IoT devices, fidelity cards, online social networks and many others. This unconventional approach is intended to find an alternative methodology to answer open questions about the human migration framework (i.e., nowcasting flows and stocks, studying the integration of multiple sources and knowledge, and investigating migration drivers). The new data have the advantage of timeliness and large geographical coverage, but also disadvantages in terms of selection bias and amount of resources required to process, as reported by Sîrbu et al. (2021) and Pollacci, Milli, Bircan, and Rossetti (2022). Therefore, models extracted from these data need to be carefully validated, typically with traditional data sources. In this context of meaningful data combination, many types of data exist, still very scattered and heterogeneous, making integration far from straightforward
Academic mobility from a big data perspective
Full text linkUnderstanding the careers and movements of highly skilled people plays an ever-increasing role in today’s global knowledge-based economy. Researchers and academics are sources of innovation and development for governments and institutions. Our study uses scientific-related data to track careers evolution and Researchers’ movements over time. To this end, we define the Yearly Degree of Collaborations Index, which measures the annual tendency of researchers to collaborate intra-nationally, and two scores to measure the mobility in and out of countries, as well as their balance
The italian music superdiversity: Geography, emotion and language: one resource to find them, one resource to rule them all
Full text linkGlobalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs’ melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices
ItEM: A Vector Space Model to Bootstrap an Italian Emotive Lexicon
Full text linkIn recent years computational linguistics has seen a rising interest in subjectivity, opinions, feelings and emotions. Even though great attention has been given to polarity recognition, the research in emotion detection has had to rely on small emotion resources. In this paper, we present a methodology to build emotive lexicons by jointly exploiting vector space models and human annotation, and we provide the first results of the evaluation with a crowdsourcing experiment
The CoLing Lab system for Sentiment Polarity Classification of tweets
Full text linkSUMMARY.
This paper describes the CoLing Lab system for the EVALITA 2014 SENTIment POLarity Classification SENTIPOLC) task. Our system is based on a SVM classifier trained on the rich set of lexical, global and twitter-specific features described in these pages. Over all, our system reached a 0.63 weighted F-score on the test set provided by the task organizers.
RIASSUNTO.
Questo contributo descrive il sistema CoLing Lab sviluppato
per il task di SENTIment POLarity Classification (SENTIPOL
C) organizzato nel contesto della campagna EVALITA
2014. Il nostro sistema è basato su un classificatore SVM addestrato sulle feature lessicali, globali e specifiche del canale twitter descritte in queste pagine. Il nostro sistema raggiunge uno score di circa
0.63 nel test set fornito dagli organizzatori del task
International mobility between the UK and Europe around Brexit: a data-driven study
Full text linkAmong the multiple effects of Brexit, changes in migration and mobility across Europe were expected. Several studies have analysed these aspects, mostly from the point of view of perceptions, motivations, economic effects, scenarios, and changes in migration from Central and Eastern European countries. In this study we propose an analysis of migration and cross-border mobility using an integrated data-driven approach. We investigate official statistics from Eurostat, together with non-traditional data, to give a more complete view of the changes after Brexit, at EU and regional level. Specifically, we employ scientific publication and Crunchbase data to study highly-skilled migration, Twitter and Air Passenger data to investigate monthly trends. While main trends are preserved across datasets, with a general decrease in migration towards the UK immediately after the referendum approval, we are able to also observe more fine grained trends specific to some data or regions. Furthermore, we relate the changes in mobility observed from Air Passenger data with attention to Brexit from Google Trends data
- …
