Quantitative Analysis of Genealogy Using Digitised Family Trees

TL;DR

This paper analyzes digitized family trees from online platforms to study population dynamics and genealogy, leveraging large-scale data and machine learning to enhance understanding of human ancestry and social trends.

Contribution

It introduces a comprehensive methodology for mining and verifying large genealogical datasets from online family trees, enabling detailed population analysis.

Findings

Insights into population sex ratios and marriage trends

Analysis of fertility, lifespan, and twin/triplet frequencies

Validation of genealogical data against census sources

Abstract

Driven by the popularity of television shows such as Who Do You Think You Are? many millions of users have uploaded their family tree to web projects such as WikiTree. Analysis of this corpus enables us to investigate genealogy computationally. The study of heritage in the social sciences has led to an increased understanding of ancestry and descent but such efforts are hampered by difficult to access data. Genealogical research is typically a tedious process involving trawling through sources such as birth and death certificates, wills, letters and land deeds. Decades of research have developed and examined hypotheses on population sex ratios, marriage trends, fertility, lifespan, and the frequency of twins and triplets. These can now be tested on vast datasets containing many billions of entries using machine learning tools. Here we survey the use of genealogy data mining using family trees dating back centuries and featuring profiles on nearly 7 million individuals based in over 160 countries. These data are not typically created by trained genealogists and so we verify them with reference to third party censuses. We present results on a range of aspects of population dynamics. Our approach extends the boundaries of genealogy inquiry to precise measurement of underlying human phenomena.