Human migration: the big data perspective

Abstract

How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.

A Deep Gravity model for mobility flows generation

“…Most of the world’s population live now in urban areas, whose evolution in structure and size influences crucial aspects of our society such as the objective and subjective well-being 6 – 11 and the diffusion of innovations 4 , 12 , 13 . It is therefore not surprising that the study of human mobility has attracted particular interest in recent years 3 , 14 – 17 , with a particular focus on the migration between cities and from rural to urban areas 18 , 19 , the study and modeling of mobility patterns in urban environments 15 , 20 – 24 , the estimation of city population 25 – 27 , the migration induced by natural disasters, climate change, and conflicts 28 – 32 , the prediction of traffic and crowd flows 14 , 33 – 37 , and the forecasting of the spreading of epidemics 38 – 42 . Human mobility modelling has important applications in these research areas.…”

Applying Concepts and Tools in Demography for Estimating, Analyzing, and Forecasting Forced Migration

“…The past decade has observed a substantial increase in studies using nontraditional data sources including mobile phone records, e-mail messages, social media data, and internet search data to estimate migration. Examples and summaries of the use of nontraditional data sources or "big data" to study migration, including discussions about strengths and challenges, can be found in Laczko and Rango (2014), Hughes et al (2016), IOM (2018), Spyratos et al (2018), IOM's GMDAC (2021), and Sîrbu et al (2021).…”

Key opportunities and challenges for the use of big data in migration research and policy

“…The quality of various data sets (e.g., demographic biases present in social media datasets) remains an unresolved challenge in teasing out comprehensive, policy-relevant results. Validating estimated migration using nearreal-time big data is also problematic, with no trusted 'gold standard' currently available (31). The slow adoption of big data analyses in the humanitarian sector is partly due to a lack of expertise in how to apply these approaches in operational settings (32).…”

“…This may result in policies which propagate stereotypes and discriminatory practices, or else continue to underserve invisible groups (e.g. , those not engaged with social media or with smaller social networks) ( 31).…”