Test and develop high resolution mapping and modelling methods to support inter-censal population estimates

Sierra Leone has recently concluded its georeferenced mid-term census that collected up-to-date, accurate and
complete demographic information on its residents. This provides a unique opportunity to compare and validate
different types of small area population estimation models and therefore inform approaches to producing
intercensal estimates going forward. By utilizing the full georeferenced mid-term census results, the proposed
research will:
(i) Test various population estimation methods using sub-samples from the mid-term census:
a. test the performance of different sample design strategies in 'bottom-up' model applications (e.g., stratification,
weighted, etc.)
b. quantify the performance of such 'bottom-up' estimates against the mid-term census when using routinely
collected surveys as inputs
c. develop and test a wide range of geospatial data (i.e., covariates) and identify the best suited for population
estimation. This will include the exploration of machine learning options to model building usage (residential/nonresidential,
and other characteristics) using high-resolution satellite imagery, building footprint data and labels and
data from various surveys.
d. identify the best method to estimate age/sex structures at high resolution,
(ii) test different types of sub-national projection methods from the last census to examine which work most
accurately and what ancillary datasets are most valuable, and
(iii) compare various top-down disaggregation of population projections with the geo-referenced census data.