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

点击翻译按钮获取中文摘要

Sierra Leone has recently concluded its georeferenced mid-term census that collected up-to-date, accurate andcomplete demographic information on its residents. This provides a unique opportunity to compare and validatedifferent types of small area population estimation models and therefore inform approaches to producingintercensal estimates going forward. By utilizing the full georeferenced mid-term census results, the proposedresearch 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 routinelycollected surveys as inputsc. develop and test a wide range of geospatial data (i.e., covariates) and identify the best suited for populationestimation. 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 anddata 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 mostaccurately and what ancillary datasets are most valuable, and(iii) compare various top-down disaggregation of population projections with the geo-referenced census data.