English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Intra-African conflicts during the collapse of the kingdom of Oyo from 1817to 1836 resulted in the enslavement of an estimated 121,000 people who werethen transported to coastal ports via complex trade networks and loaded ontoslave ships destined for the Americas. Historians have a good record of wherethese people went across the Atlantic, but little is known about whereindividuals were from or enslaved \textit{within} Africa. In this work, wedevelop a novel two-step statistical approach to describe the enslavement ofpeople given documented violent conflict, the transport of enslaved peoplesfrom their location of capture to their port of departure, and---given anenslaved individual's location of departure---that person's probability oforigin. We combine spatial prediction of conflict density via Kriging with aMarkov decision process characterising intra-African transportation. Theresults of this model can be visualised using an interactive web application,plotting estimated conditional probabilities of historical migrations duringthe African diaspora. These results help trace the uncertain origins of peopleenslaved in this region of Africa during this time period: using the two-stepstatistical methodology developed here provides a probabilistic answer to thisquestion.

Estimating conditional probabilities of historical migrations in the transatlantic slave trade using kriging and Markov decision process models