A1 Refereed original research article in a scientific journal
Creating a Historical Migration Dataset from Finnish Church Records, 1800–1920
Authors: Vesalainen, Ari; Kanerva, Jenna; Nitsch, Aïda; Korsu, Kiia; Larkiola, Ilari; Ruotsalainen, Laura; Ginter, Filip
Publisher: Ubiquity Press, Ltd.
Publication year: 2025
Journal:: Journal of Open Humanities Data
Article number: 48
Volume: 11
eISSN: 2059-481X
DOI: https://doi.org/10.5334/johd.345
Web address : https://doi.org/10.5334/johd.345
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/500285856
This article presents a large-scale effort to create a structured dataset of internal migration in Finland between 1800 and 1920 using digitized church moving records. These records, maintained by Evangelical-Lutheran parishes, document the migration of individuals and families and offer a valuable source for studying historical demographic patterns. The dataset includes over six million entries extracted from approximately 200,000 images of handwritten migration records. The data extraction process was automated using a deep learning pipeline that included layout analysis, table detection, cell classification, and handwriting recognition. The complete pipeline was applied to all images, resulting in a structured dataset suitable for research. The dataset can be used to study internal migration, urbanization, and family migration, and the spread of disease in preindustrial Finland. A case study from the Elimaki parish shows how local migration histories can be reconstructed. The work demonstrates how large volumes of handwritten archival material can be transformed into structured data to support historical and demographic research.
Downloadable publication This is an electronic reprint of the original article. |
Funding information in the publication:
This work was supported by the Human Diversity consortium, Profi7 program by Research Council of Finland (grant 352727) as well as the HPC-HD Research Council of Finland general research grant (grant 347708).