Migration is Made Easy with the Help of Machine Learning
The migration process is made easy with the help of a machine learning
Almost every country around the world is currently facing problems regarding migrated people. To find a solution for this immigration and refugee problem, nations are lining up to get help from artificial intelligence. AI in global immigration is helping countries to automate a plethora of decisions that are made almost daily as people want to cross borders and look for new homes. AI can help in the prediction of the next migrating crisis that may arise on the borders of a nation. AI can monitor people’s movement through Wi-Fi positioning, google trends, etc. It also helps the countries to prepare themselves for mass migration. Governments can use AI algorithms to examine huge datasets and look for potential gaps in their reception facilities such as the absence of appropriate places for people or vulnerable unaccompanied children. Based on sample data, machine learning algorithms can build mathematical models to make predictions about migration without being explicitly programmed to perform a specific task. For instance, recent projects have deployed machine learning for various goals, such as to predict migration flows from specific countries, assess the perception of refugees in host communities, and support analyses of satellite imagery of refugee camps.
Traditional human mobility models, such as gravity models or the more recent radiation model, predict human mobility flows based on population and distance features only. These models have been validated on commuting flows, a different type of human mobility, and are mainly used in modelling scenarios where large amounts of prior ground truth mobility data are not available. One downside of these models is that they have a fixed form and are therefore not able to capture more complicated migration dynamics. We propose machine learning models that can incorporate any number of exogenous features, to predict origin/destination human migration flows. The machine learning models outperform traditional human mobility models on a variety of evaluation metrics, both in the task of predicting migrations between US counties as well as international migrations. In general, predictive machine learning models of human migration will provide a flexible base with which to model human migration under different what-if conditions, such as potential sea-level rise or population growth scenarios.
A great example of such use of machine learning in migration is given by Sameer Mahajan in his writing at slab. He has talked about how he came up with a machine learning model as a neighborhood recommender. The purpose of this model was to find a suitable neighborhood with the help of a few conditions in a new place. For example, if someone is looking for a quiet neighborhood, a place nearer to the office, and a doctor’s place nearby, the ML model would simply use those categories to find out the suitable neighborhood. The system is very simple. One would have to pick cities, neighborhoods, decide the number of venues to be compared, and let Machine Learning kick in to recommend matching communities. It shows the map and also lists all the points of comparison.