Back to Search Start Over

A modelling strategy to estimate conditional probabilities of African origins: The collapse of the Oyo Empire and the transatlantic slave trade, 1817–1836.

Authors :
Wiens, Ashton
Lovejoy, Henry B.
Mullen, Zachary
Vance, Eric A.
Source :
Journal of the Royal Statistical Society: Series A (Statistics in Society); Jul2022, Vol. 185 Issue 3, p1247-1270, 24p, 1 Diagram, 2 Graphs, 2 Maps
Publication Year :
2022

Abstract

Intra‐African conflicts during the collapse of the kingdom of Oyo from 1817 to 1836 resulted in the enslavement of an estimated 121,000 people who were then transported to coastal ports via complex trade networks and loaded onto slave ships destined for the Americas. Historians have a good record of where these people went across the Atlantic, but little is known about where individuals were from or enslaved within Africa. In this work, we develop a novel statistical modelling strategy to describe the enslavement of people given documented violent conflict, the transport of enslaved peoples from their location of capture to their port of departure, and—given an enslaved individual's location of departure—that person's probability of origin. We combine spatial prediction of conflict density via kriging with a Markov decision process characterising intra‐African transportation. The results of this model can be visualised using an interactive web application to plot—for the first time—estimated conditional probabilities of historical origins during the African diaspora. Understanding the likely origins within Africa of enslaved people may have ramifications for the history of the Atlantic world, whereby the ocean connects, rather than disconnects, Africa, the Americas, and Europe. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09641998
Volume :
185
Issue :
3
Database :
Complementary Index
Journal :
Journal of the Royal Statistical Society: Series A (Statistics in Society)
Publication Type :
Academic Journal
Accession number :
158201692
Full Text :
https://doi.org/10.1111/rssa.12833