ArticlePloS one2020
Migrant mobility flows characterized with digital data.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- A highly granular temporary migration dataset derived from mobile phone data in Senegal.Scientific data · 2025Article
- Estimation of internal displacement in Ukraine from satellite-based car detections.Scientific reports · 2024Article
- The Venezuelan Humanitarian Crisis, Out-Migration, and Household Change Among Venezuelans in Venezuela and Abroad.Demography · 2024Article
- Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela.Spatial and spatio-temporal epidemiology · 2022Article
- Mobile phone data reveal the effects of violence on internal displacement in Afghanistan.Nature human behaviour · 2022Article
- The potential of Facebook advertising data for understanding flows of people from Ukraine to the European Union.EPJ data science · 2022Article
- Using Facebook advertising data to describe the socio-economic situation of Syrian refugees in Lebanon.Frontiers in big data · 2022Article
- How do city-specific factors affect migrant integration in China? A study based on a hierarchical linear model of migrants and cities.PloS one · 2021Article
- A data fusion approach to the estimation of temporary populations: An application to Australia.PloS one · 2021Article
- An approach to integrate population mobility patterns and sociocultural factors in communicable disease preparedness and response.Humanities & social sciences communications · 2021Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monitoring migration flows is crucial to respond to humanitarian crisis and to design efficient policies. This information usually comes from surveys and border controls, but timely accessibility and methodological concerns reduce its usefulness. Here, we propose a method to detect migration flows worldwide using geolocated Twitter data. We focus on the migration crisis in Venezuela and show that the calculated flows are consistent with official statistics at country level. Our method is versatile and far-reaching, as it can be used to study different features of migration as preferred routes, settlement areas, mobility through several countries, spatial integration in cities, etc. It provides finer geographical and temporal resolutions, allowing the exploration of issues not contemplated in official records. It is our hope that these new sources of information can complement official ones, helping authorities and humanitarian organizations to better assess when and where to intervene on the ground.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.