Abstract
Over the past two decades, a huge number of historical documents have been digitised and made available online. At the same time, numerous software options and websites have encouraged people to conduct research into their family trees, leading to a surge in the availability of genealogical data. A major advantage of genealogical data, from a scientific research perspective, is that it combines information from many sources into a format that is structured by family relations and descendancy, which is very useful for studying the dynamics of population change over the generations. A critical issue for researchers who want to use genealogical data is how to assess the quality of the data and put in place measures to correct the errors that we find in it. In this chapter, I present some of the methods that are being used to filter, clean and aggregate genealogical data to create large datasets that may be used across a diverse range of academic research disciplines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
According to Louis Kessler, an expert on the GEDCOM format, speaking at Gaenovium 2014, a genealogy technology conference held on 7 October 2014 in Leiden, The Netherlands.
- 2.
Dutch NWO funded project conducted at University of Utrecht: Nature or nurture? A search for the institutional and biological determinants of life expectancy in Europe during the early modern period (276-53-008).
References
Bhattacharya, I., & Getoor, L. (2007). Query-time entity resolution. Journal of Artificial Intelligence Research, 30, 621–657.
Christen, P. (2012). Data matching. Berlin: Springer. doi:10.1007/978-3-642-31164-2
Fu, Z., Christen, P., & Boot, M. (2011). A supervised learning and group linking method for historical census household linkage. In Proceedings of the Ninth Australasian Data Mining Conference (Vol. 121, pp. 153–162). Australian Computer Society, Inc.
Gavrilov, L. A. & Gavrilova, N. S. (2001). Biodemographic Study of Familial Determinants of Human Longevity. Population: An English Selection, 13(1), 197–221.
Gavrilova, N. S., & Gavrilov, L. A. (2007). Search for predictors of exceptional human longevity. North American Actuarial Journal, 11(1), 49–67. doi:10.1080/10920277.2007.10597437
Gellatly, C. (2009). Trends in population sex ratios may be explained by changes in the frequencies of polymorphic alleles of a sex ratio gene. Evolutionary Biology, 36(2), 190–200. doi:10.1007/s11692-008-9046-3
Ivie, S., Pixton, B., & Giraud-Carrier, C. (2007). Metric-based data mining model for genealogical record linkage. In IRI 2007, IEEE international Conference on Infomation Reuse and Integration.
Larmuseau, M. H. D., Van Geystelen, A., van Oven, M., & Decorte, R. (2013). Genetic genealogy comes of age: Perspectives on the use of deep-rooted pedigrees in human population genetics. American Journal of Physical Anthropology, 150(4), 505–511. doi:10.1002/ajpa.22233
Moreau, C., Bhérer, C., Vézina, H., Jomphe, M., Labuda, D., & Excoffier, L. (2011). Deep human genealogies reveal a selective advantage to be on an expanding wave front. Science, 334(6059), 1148–1150. doi:10.1126/science.1212880
Newcombe, H. B., Kennedy, J. M., Axford, S. J., & James, A. P. (1959). Automatic linkage of vital records: Computers can be used to extract “follow-up” statistics of families from files of routine records. Science, 130(3381), 954–959. doi:10.1126/science.130.3381.954
Otterstrom, S. M., & Bunker, B. E. (2013). Genealogy, migration, and the intertwined geographies of personal pasts. Annals of the Association of American Geographers, 103(3), 544–569. doi:10.1080/00045608.2012.700607
Post, W., van Poppel, F., van Imhoff, E., & Kruse, E. (1997). Reconstructing the extended kin-network in the Netherlands with genealogical data: methods, problems, and results. Population Studies, 51(3), 263–278. doi:10.1080/0032472031000150046
United Nations. (1983). Manual X: Indirect techniques for demographic estimation. United Nations Publication.
Zhao, Z. (1994). Demographic conditions and multi-generation households in Chinese history. Results from genealogical research and microsimulation. Population Studies, 48(3), 413–425. doi:10.1080/0032472031000147946
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Gellatly, C. (2015). Reconstructing Historical Populations from Genealogical Data Files. In: Bloothooft, G., Christen, P., Mandemakers, K., Schraagen, M. (eds) Population Reconstruction. Springer, Cham. https://doi.org/10.1007/978-3-319-19884-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-19884-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19883-5
Online ISBN: 978-3-319-19884-2
eBook Packages: Humanities, Social Sciences and LawSocial Sciences (R0)