Dataset of Multi-aspect Integrated Migration Indicators
D. Goglia (1), L. Pollacci (1), A. Sirbu (1) ((1) Computer Science, Department, University of Pisa, Pisa, Italy)

TL;DR
The paper introduces MIMI, a comprehensive dataset combining traditional migration data with novel indicators like social networks, aiming to enhance the analysis and forecasting of migration trends.
Contribution
It presents a new multidisciplinary dataset that merges official migration data with innovative indicators such as online social networks for migration studies.
Findings
The dataset integrates traditional and novel migration indicators.
It enables improved nowcasting and forecasting of migration trends.
The dataset supports multidisciplinary migration research.
Abstract
Nowadays, new branches of research are proposing the use of non-traditional data sources for the study of migration trends in order to find an original methodology to answer open questions about the human mobility framework. In this context we presents the Multi-aspect Integrated Migration Indicators (MIMI) dataset, an new dataset of migration drivers, resulting from the process of acquisition, transformation and merge of both official data about international flows and stocks and original indicators not typically used in migration studies, such as online social networks. This work describes the process of gathering, embedding and merging traditional and novel features, resulting in this new multidisciplinary dataset that we believe could significantly contribute to nowcast to forecast both present and future bilateral migration trends.
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Migration and Labor Dynamics · Data-Driven Disease Surveillance
