From 1b3ac35c6b3588ccfe14c6b2bc2bee78a3c6cfc5 Mon Sep 17 00:00:00 2001 From: Diletta Goglia Date: Fri, 3 Jun 2022 20:20:08 +0200 Subject: [PATCH] Update README.md --- README.md | 79 +++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 77 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 211c6df..3f94fad 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,77 @@ -# 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.1, Pollacci, L.1, Sirbu, A.1 + +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) + +## 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) DOI +- 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) + + + + + +## 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.
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.
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, “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. + +

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+ + + + + +## Contact +**Dr. Diletta Goglia**
+**Postgraduate Student in MSc in Artificial Intelligence**
+**Computer Science department, University of Pisa, Italy**
+[d.goglia@studenti.unipi.it](mailto:d.goglia@studenti.unipi.it)
+[dilettagoglia.netlify.app](www.dilettagoglia.netlify.app) + + +## License +Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License. + +_Last update: June 3rd, 2022_ +