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Inferring migrations, traditional methods and new approaches based on mobile phone, social media, and other big data
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Inferring migrations, traditional methods and new approaches based on mobile phone, social media, and other big data

Feasibility study on inferring (labour) mobility and migration in the European Union from big data and social media data

Inferring migrations, traditional methods and new approaches based on mobile phone, social media, and other big data

Feasibility study on inferring (labour) mobility and migration in the European Union from big data and social media data

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This report addresses the question of whether it is technically, financially and legally feasible to estimate geographic mobility and migration flows in the European Union. Our assessment indicates that the feasibility is dependent on a number of factors: 1. It depends on the data that one can have access to. Some data sources can be accessed by anyone with the appropriate technical skills (e.g., samples of Twitter tweets); some can be purchased (e.g., historical tweets); some are not for sale and require partnerships with companies (e.g., Yahoo!, Facebook, LinkedIn, and mobile phone providers); some are not shared by companies (Google does not share data, except for some aggregate indexes, like the ones in Google Trends). 2. It depends on the outcome desired. Estimating trends or changes in trends in migration flows is feasible and can be done in a timely manner. Getting accurate and precise estimates for special populations, like refugees, may or may not be feasible depending on the context: it would require further research. Likewise, obtaining estimates of short-term migration by education, gender or employment status is feasible. Obtaining unbiased estimates of short-term mobility from a single, non-representative source would be more difficult. It may be feasible in some circumstances (e.g., when the data set is rich enough for the use of post-stratification techniques), but not in others. 3. It depends on legal obstacles. Companies may have terms and conditions or non- disclosure agreements for data sharing that may or may not include inconsistencies with the rules governing universities and funding agencies. We have not identified major issues in this area, but each individual collaboration across units would require some careful examination of the terms and conditions in order to resolve any potential lack of consistency.
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