Predicting Refugee Flows from Ukraine with an Approach to Big (Crisis) Data: A New Opportunity for Refugee and Humanitarian Studies

Abstract

This study was created due to the need to predict the migration flows of refugees from Ukraine to the EU in the absence of official data. We present a descriptive analysis of Big Data sources, which are helpful in determining, as well as for estimating and forecasting refuge emigration flows from Ukraine and help crisis managers. The objective of this study was to test the usefulness of Big Data and Google Trends (GT) indexes to predict further forced migration from Ukraine to the EU (mainly to Germany). The primary methodological concept of our approach is to monitor the digital trace of Internet searches in Ukrainian, Russian and English with the GT analytical tool. The control mechanism for testing this sort of Big Data was performed by comparing those insights with the official databases from UNHCR and national governments, which were available two months later. All tested migration-related search queries (20) about emigration planning from Ukraine show a positive linear association between the Google index and data from official UNHCR statistics; R2 = 0.1211 for searches in Russian and R2 = 0.1831 for searches in Ukrainian. Increase in migration-related search activities in Ukraine, such as “граница” (Rus. border), кордону (Ukr. border); “Польща” (Poland); “Германия” (Rus. Germany), “Німеччина” (Ukr. Germany) and “Угорщина” and “Венгрия” (Hungary) correlate strongly with officially UNHCR data for externally displaced persons from Ukraine. The results show that one-fourth of all refugees will cross into Germany. According to Big Data insights, the estimated number of expected refugees until July 2022 is 5.9 Million refugees and mid-2023 Germany can expect 1.5 million Ukrainian refugees. Keywords: refugee, forecasting refugee flows, Ukraine, big data, Google trends, forced migration, UNHCR