Data Science for Migration and Mobility
Contents
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Introduction Introduction
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Relocation and resettlement of refugees and asylum seekers in Europe Relocation and resettlement of refugees and asylum seekers in Europe
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Data-driven migration planning Data-driven migration planning
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Method Method
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Predicting individual acceptance Predicting individual acceptance
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Predicting social acceptance Predicting social acceptance
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Results Results
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Discussion and conclusions Discussion and conclusions
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Acknowledgements Acknowledgements
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References References
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14 Using Machine Learning and Synthetic Populations to Predict Support for Refugees and Asylum Seekers in European Regions
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Published:10 November 2022
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Abstract
The massive arrival of forced immigrants in Europe is generating an unprecedented political and demographic impact. In recent years, organisations such as UNHCR have made efforts to resettle and relocate these displaced people within the European borders. This work aims to predict on a large scale which regions are most likely to support refugees and asylum seekers through big data analysis techniques, understanding that this is an indicator of social acceptance and, in turn, an important variable to consider in the resettlement and relocation processes. To do this, we modelled data from public opinion surveys (Eurobarometer) on attitudes towards these groups, using supervised machine learning algorithms to estimate the probability that an artificial citizen supports the reception of asylum seekers in their country. Subsequently, we used simulation to build synthetic populations based on Eurostat census data (at NUTS 2 level) throughout Europe to estimate the aggregate probabilities for each geographic region. We specifically simulate the sociodemographic characteristics of 2,710,000 European citizens, corresponding to 10,000 in each of the 271 NUTS 2 regions. In the results, we generate the first articulated data that estimate the future social integration of refugees and asylum seekers in all basic regions of Europe.
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