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README.md

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-# Non-traditional_data_sources_to_nowcast_migration_trends_through_AI
-🎓Research project 
+# Use of non-traditional data sources to nowcast migration trends through Artificial Intelligence technologies.
+
+
+#### Goglia, D.<sup>1</sup>, Pollacci, L.<sup>1</sup>, Sirbu, A.<sup>1</sup>
+
+<sup>1</sup> 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)
+
+## Short abstract
+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.
+
+
+## Resources
+| MIMI dataset  | Linear Regression | Neural Model | Talks
+| ------------- | ----------------- | ------------ | -----
+| [Code](https://github.com/dilettagoglia/MIMI-dataset)  | [Code](https://github.com/dilettagoglia/OLS-model) | _Please wait for the content_  | [Slides](https://github.com/dilettagoglia/Non-traditional_data_sources_to_nowcast_migration_trends_through_AI/slides)
+| [Download](https://doi.org/10.5281/zenodo.6493325)  | [Documentation](https://www.statsmodels.org/dev/regression.html) | _Please wait for the content_  | [Abstract](https://github.com/dilettagoglia/Non-traditional_data_sources_to_nowcast_migration_trends_through_AI/abstracts) 
+
+
+## Related publications
+- Goglia, D. (2022) "Multi-aspect Integrated Migration Indicators (MIMI) dataset", v2.0. Zenodo. [10.5281/zenodo.6493325](https://doi.org/10.5281/zenodo.6493325) <a href="https://doi.org/10.5281/zenodo.6493325"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.6493325.svg" alt="DOI"></a>
+- 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](https://arxiv.org/abs/2204.14223) [![arXiv](https://img.shields.io/badge/arXiv-2204.14223-b31b1b.svg?style=flat)](https://arxiv.org/abs/2204.14223)
+<!-- paper 2 -->
+<!-- Oxford conference proceedings -->
+<!-- master thesis -->
+
+
+## Talks
+This work was presented during the following events:
+- [“Digitization of Migration Research Methods: Promises and Pitfalls”](https://www.imiscoe.org/news-and-blog/news/news-from-members/1426-call-for-papers-workshop-digitization-of-migration-research-methods-promises-and-pitfalls), organised by Warsaw Centre of Migration Research (CMR) & University of Warsaw | May 30th, 2022 | Centre of New Technologies University of Warsaw, Warsaw, Poland. <br/> Corresponding panel: "Session 2: Mixing and/or combining – new considerations for the digital era". 
+- [“Measuring Migration: How? When? Why?”](https://www.torch.ox.ac.uk/event/call-for-papers/submissions-mmn-conference-measuring-migration-how-when-why?fbclid=IwAR0ML5v0ANKyZKBb572EO8ZEuzpV7HQJA-eCCBuclAVq6uO9N53BWmmN4YI), organised by University of Oxford’s Migration and Mobility Network & Nuffield College | June 9th and 10th, 2022 | Nuffield College, University of Oxford, Oxford, GB. <br/> Corresponding panel: "Session 1a: How do we measure migration? Methods and advancements".
+
+
+## Funding
+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, <a href='https://plusplus.sobigdata.eu/'>“SoBigData++:
+European Integrated Infrastructure for Social Mining and Big Data Analytics”</a>, and by the Horizon2020
+European projects <a href='https://hummingbird-h2020.eu/'>“HumMingBird – Enhanced migration measures from a multidimensional perspective”</a>,
+Grant Agreement n. 870661.
+
+<p align="center">
+  <a href='https://plusplus.sobigdata.eu/'>
+    <img align="center" src=https://cis.cnrs.fr/wp-content/uploads/2020/03/logo-sobigdata.png width="180px">
+  </a>
+</p>
+
+<p align="center">
+  <a href='https://hummingbird-h2020.eu/'>
+    <img align="center" src=https://hummingbird-h2020.eu/phantasy-root-skin/hummingbird_logo_120h.png width="180px">
+  </a
+</p>
+
+
+## Contact
+**Dr. Diletta Goglia** <br/>
+**Postgraduate Student in MSc in Artificial Intelligence** <br/>
+**Computer Science department, University of Pisa, Italy** <br/>
+[d.goglia@studenti.unipi.it](mailto:d.goglia@studenti.unipi.it) <br/>
+[dilettagoglia.netlify.app](www.dilettagoglia.netlify.app) 
+
+
+## License
+<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
+
+_Last update: June 3rd, 2022_
+