Editorial: New Methodological Approaches for Migration and Mobility Studies: From Traditional to Big Data

Tuba Bircan^1*Haodong Qi²

• ¹Vrije University Brussels, Brussels, Belgium
• ²Malmo universitet, Malmö, Sweden

In their article, Nalbandian and Dreher (2023) provide a comprehensive review of advanced digital technologies in migration management. They chart the growing use of biometrics, artificial intelligence, mobile analytics, and drones across different regions and governance domains. Their analysis exposes a striking gap: while technological experimentation is widespread, systematic technical evaluation is rare. Descriptive accounts dominate, particularly in high-stakes domains such as border surveillance and asylum processing. Without calibration against independent benchmarks, the risk is techno-solutionism, policies justified by tools whose performance is neither transparent nor reproducible. The review calls for deeper scrutiny, interdisciplinary collaboration, and attention to the power asymmetries that shape how technologies are deployed.Where Nalbandian and Dreher highlight the risks of under-evaluation, Umel (2024) shows how calibration can also be interpretative. Combining topic modelling with ethnographically informed qualitative analysis, and grounding both in social representations theory, the study examines Filipino migrants' Facebook exchanges in Germany. Rather than treating automated topics as answers, the approach positions them as pointers requiring contextual interpretation. This reflexive design demonstrates that calibration is not limited to statistical benchmarking but also involves anchoring computational outputs in theory and meaning. In multilingual and culturally dense settings, validity arises not from scale but from aligning methods with lived realities. Kondyli, Nisiotis, and Klironomos (2024) remind us that calibration is not only a methodological practice but also an infrastructural achievement. Using the SoDaNet repository in Greece as a case study, they show how rigorous documentation, anonymisation, versioning, and persistent identifiers enable secondary analysis and replication. Their demonstration of "secondary added value" highlights how indices, replications, and derivative datasets themselves become future benchmarks. Calibration therefore depends on more than individual diligence: it requires repositories and infrastructures that embed transparency and reusability in the architecture of migration research. This echoes broader calls for FAIR data principles to become operational realities rather than aspirational slogans.Ahmad Yar and Bircan (2025) provide a state-of-the-art synthesis on the role of big data in migration statistics. They show that digital traces, social media activity, mobile phone records, and satellite imagery, have genuine strengths in timeliness and granularity, and have advanced nowcasting, short-term mobility mapping, and integration research. However, the value of these innovations lies not in isolated applications but in systematic approaches that combine computational methods with established demographic infrastructures (Salah, Korkmaz, & Bircan, 2022). Yet persistent weaknesses remain: digital divides, unstable denominators, proprietary data access, and a several lack of detailed demographic information of migrants. Their conclusion is clear. Big data has scientific value not in isolation but when triangulated with registers, surveys, and administrative data, and when models are validated against credible counterfactuals. Calibration is thus the route from experimental innovation to policy relevance. When considered as a whole in this research topic, the papers point to a shared agenda for methodological progress. First, validation against trusted benchmarks is a must, not an optional add-on. These benchmarks continue to rely on long-standing instruments such as censuses, registers, and household surveys, which remain the backbone of official migration statistics worldwide (UN DESA, 2020). Without systematic comparison to such sources, computational estimates remain largely uncertain and questionable. Second, reflexivity must be treated as integral to calibration. Automated analyses of online discourse or mobility traces cannot stand alone; they must be anchored in theoretical and contextual interpretation to avoid decontextualisation or cultural misreading. Third, infrastructures for documentation and reuse are essential to make calibration sustainable, turning individual good practice into collective scientific standards. Infrastructures such as repositories, metadata systems, and replication protocols extend calibration from a scholarly habit to a field-wide norm.While big data holds potential for migration research, it cannot substitute for traditional sources. A full transition 'from traditional to big data' is neither feasible nor desirable. What emerges instead is complementary methodological innovations; traditional sources continue to offer comparability, demographic depth, and legal anchoring, meanwhile, big data sources provide timely and granular information, enabling deeper insights into the spatial-temporal patterns of human mobility. Qualitative and participatory approaches, on the other hand, add context, ensuring that numerical findings are interpretable. Calibration is the practice that holds these strands together, through adaptive statistical frameworks that can integrate prior knowledge into statistical analysis, mandates for sharing code and metadata ensuring reproducible numerical findings, and ethics-by-design protocols that protect individuals while allowing verification. The calibration process proposed here by no means intends to address all aspects of limitations and challenges in migration research. Some populations will remain difficult to observe and some data will remain inaccessible, leading to incomplete adjustments for sample selection and other biases. But the lesson from this Research Topic is clear. When new and traditional sources are aligned, findings become more credible, contestable, and reusable. This is the path by which methodological innovation strengthens not only predictive accuracy but also the evidentiary base for informed public debate on migration. The challenge now is to make calibration common practice: not an afterthought, but a shared standard for research that aspires to shape policy and scholarship alike.