Introducing the EUMIGRATOOL (EMT)

The EMT is a solution-oriented tool with two main functions:
(I) Predicting migration flows
(II) Detecting risks of tensions related to migration

The backend of EMT comprises two complementary approaches to:

Simulation

The Small-Scale Model (SSM) aims to predict the distribution of incoming asylum-seekers/unrecognised refugees arriving to neighbouring countries of conflict origins. It uses a generalised and automated simulation development approach and the Flee agent-based simulation code, which is optimised for simplicity and flexibility. The SSM synthesises data from the United Nations High Commissioner for Refugees (UNHCR), the Armed Conflict Location and Event Data Project (ACLED), OpenStreetMap and population data using the City Population database or other population sources. The conflict model is constructed, run and validated by comparing the simulation results to the existing camp registrations obtained from UNHCR.

Forecasting

The Large-Scale Model (LSM) produces monthly predictions of asylum applications in the EU for a variety of bilateral (i.e., from country of origin to the EU Member State) cases. It uses state of the art machine learning approaches, including neural network architectures and time series analysis. Its techniques allow for correlation analysis between raw data sources and simulation. Furthermore, the LSM provides intuitions on attitudes towards migration among populations in all European destination countries, using the Twitter Sentiment Analysis model data as input, and the most influential or relevant determinants of attitudes towards migration. The LSM combines a set of different inputs and methods from Topic Modeling by monitoring national press and asylum seeker data from Eurostat (the official EU statistics office).

emt

The EMT includes analysis of media content from TV-news (video content), web-news and social media (text content) using deep learning and proprosing novel deep architectures in generative modelling and forecasting using sequential data. Predictions incorporate algorithms that consider the two key chanllenges associated with prediction of migration: (a) Adequate selection of relevant data sources, and (b) correct selection of the potencial drivers to be monitored and to the warning thresholds to be set.

The backend of EMT comprises two complementary approaches to:

helping end-users to further analyse mixed migration flows and to distinguish persons in need ofInternational Protection (eligible for asylum status) fromthose in different migration situations who, in theabsence of legal routes, use illegal methods.

EMT is able to account for entry quote when forecasting migratory flows, having a real numberof migrants arriving to a particular country and region, helping NGOs understand the humaneffort and material resources that need to be allocated in that particular territory beforethe arrival.

Would you like to find out more about ITFLOWS?

Check out this short video of how the project will help in managing migration flows to the EU!