A Classification and Data Visualization Tool Applied to Human Migration Analysis

Nowadays, in a highly-globalized world, the understanding of causes and consequences involved in the migration phenomena, and also the prediction of migration flows are important for development of national public policies and for urban resource planning. The high complexity of human im/emigration movements can not only be explained by economic causes but rather by the interaction among multiple additional factors (demographic, social, linguistic, among others). The application of Machine Learning techniques and Data Visualization models on high volumes of raw data from countries can provide good insight to understand how indicators from countries are related to migration causes, and also to make visible the migration flows between the sending and receiving countries. This paper describes a tool which includes supervised classification and visualization methods to analyze country indicators and aim to discover the connections among these attributes and the migration movements.