  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.
