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The advent of modern computers has added an increased emphasis on channelingcomputational power and statistical methods into digital humanities. Includingincreased statistical rigor in history poses unique challenges due to theinherent uncertainties of word-of-mouth and poorly recorded data. Africangenealogies form an important such example, both in terms of individualancestries and broader historical context in the absence of written records.Our project aims to bridge the lack of accurate maps of Africa during thetrans-Atlantic slave trade with the personalized question of where withinAfrica an individual slave may have hailed. We approach this question with atwo part mathematical model informed by two primary sets of data. We begin witha conflict intensity surface which can generate capture locations oftheoretical slaves, and accompany this with a Markov decision process whichmodels the transport of these slaves through existing cities to the coastalareas. Ultimately, we can use this two-step approach of providing capturelocations to a historical trade network in a simulative fashion to generate andvisualize the conditional probability of a slave coming from a certain spatialregion given they were sold at a certain port. This is a data-driven visualanswer to the research question of where the slaves departing these portsoriginated.

Mapping the uncertainty of 19th century West African slave origins using a Markov decision process model