Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model

Population data is crucial for policy decisions, but fine-scale population numbers are often lacking due to the challenge of sharing sensitive data. Different approaches, such as the use of the Random Forest (RF) model, have been used to disaggregate census data from higher administrative units to small area scales. A major limitation of the RF model is its inability to quantify the uncertainties associated with the predicted populations, which can be important for policy decisions. In this study, we applied a Bayesian Additive Regression Tree (BART) model for population disaggregation and compared the result with a RF model using both simulated data and the 2021 census data for Ghana. The BART model consistently outperforms the RF model in out-of-sample predictions for all metrics, such as bias, mean squared error (MSE), and root mean squared error (RMSE). The BART model also addresses the limitations of the RF model by providing uncertainty estimates around the predicted population, which is often lacking with the RF model. Overall, the study demonstrates the superiority of the BART model over the RF model in disaggregating population data and highlights its potential for gridded population estimates. • We compare the Random Forest (RF) and the Bayesian Additive Regression Tree (BART) models for population disaggregation. • The BART model performs better for disaggregating the population compared to the RF model. • BART provides uncertainty estimates around the disaggregated population, which is often lacking with the RF model. • This study highlights the BART model's superiority in disaggregating population data for policy decisions.