Use of non-traditional data sources to nowcast migration trends through Artificial Intelligence technologies.
1 Department of Computer Science, University of Pisa, 56127 Pisa, Italy; d.goglia@studenti.unipi.it (D.G.), laura.pollacci@di.unipi.it (L.P.), alina.sirbu@unipi.it (A.S.)
In 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. Many researchers have proposed to employ non-traditional data sources to study migration trends, including so-called social Big Data such as online social networks. This unconventional approach is intended to find an alternative methodology to answer open questions about the human mobility framework (i.e., nowcasting flows and stocks, studying the integration of multiple sources and knowledge, and investigating migration drivers). In this context of meaningful data combination, many types of data exist, still very scattered and heterogeneous, making integration far from straightforward. Our work focuses on the integrated use of heterogeneous traditional datasets and new data types. We present two different contributions: a new multi-feature dataset (MIMI dataset) and a new predictive model that could significantly contribute to the study of migration drivers and to forecast emerging trends through the use of Artificial Intelligence technologies. All in all, our contribution lie in the need for new perspectives, methods, and analyses that can no longer prescind from taking into account a variety of new factors. The heterogeneous and multidimensional sets of data released with MIMI and exploited in the two models with the aid of the BMP indicator offer a new overview of the characteristics of human migration, enabling a better understanding and potential exploration of the relationship between migration and its drivers also through non-traditional sources of data.
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- Goglia, D. (2022) "Multi-aspect Integrated Migration Indicators (MIMI) dataset", v2.0. Zenodo. 10.5281/zenodo.6493325
- Goglia, D., Pollacci, L., Sirbu, A. (2022) "Dataset of Multi-aspect Integrated Migration Indicators", submitted to MDPI Data, ArXiv pre-print available at https://arxiv.org/abs/2204.14223
The different stages of this work were presented during the following events:
| Date | Event | Organiser | Location | Panel |
|---|---|---|---|---|
| October 19th, 2022 | Migration and Mobility Research in the Digital Era (MIMODE 2022), a satellite event of the "Conference on Complex Systems 2022" | Max Planck Institute for Demographic Research | Palma De Mallorca, Illes Balears, Spain | |
| October 10th and 11th, 2022 | New data and methods for migration studies: going beyond traditional data sources workshop | Paris School of Economics, SoBigData++ consortium, HumMingBird consortium and Institut Convergences Migrations | Paris School of Economics, Paris, France | Session 4: Methods |
| June 13th and 14th, 2022 | HumMingBird Consortium Meeting | HumMingBird Consortium | University of Pisa, Pisa, Italy | |
| June 9th and 10th, 2022 | “Measuring Migration: How? When? Why?” conference | University of Oxford’s Migration and Mobility Network & Nuffield College | Nuffield College, University of Oxford, Oxford, GB | Session 1a: How do we measure migration? Methods and advancements |
| May 30th, 2022 | “Digitization of Migration Research Methods: Promises and Pitfalls” workshop | Warsaw Centre of Migration Research (CMR) & University of Warsaw | Centre of New Technologies University of Warsaw, Warsaw, Poland | Session 2: Mixing and/or combining – new considerations for the digital era |
| December 9th and 10th, 2021 | HumMingBird Consortium Meeting | HumMingBird Consortium | University of Salamanca, Salamanca, Spain |
This work is supported by the European Union – Horizon 2020 Program under the scheme “INFRAIA-01- 2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics”, and by the Horizon2020 European projects “HumMingBird – Enhanced migration measures from a multidimensional perspective”, Grant Agreement n. 870661.
Diletta Goglia 
Postgraduate Student in MSc in Artificial Intelligence
Computer Science department, University of Pisa, Italy
✉️d.goglia@studenti.unipi.it
🌐dilettagoglia.netlify.app
This work is licensed under a Creative Commons Attribution 4.0 International License.
If you use the library in an academic setting, please cite the following paper:
Goglia, D., Pollacci, L., Sirbu, A. (2022) "Dataset of Multi-aspect Integrated Migration Indicators", ArXiv, 10.48550/arXiv.2204.14223
@article{Goglia2022,
Author = {Diletta Goglia and Laura Pollacci and Alina Sirbu},
Title = {Dataset of Multi-aspect Integrated Migration Indicators},
Year = {2022},
Doi = {10.48550/arXiv.2204.14223},
Url = {https://arxiv.org/abs/2204.14223},
Journal = {{ArXiv}}}Last update: October, 2022