Small area population forecasting using geospatial big datasets and national census in low and middle income countries

Africa's population is forecasted to double over the next 40 years, contributing to a high rate of
urbanisation, and as a consequence, this trigger environmental, social-economic and health variations.
Small geographic area details are crucial to plan and be abreast of these changes. Despite their
importance, small area forecasts remain under-explored which limit the potential of low and middle
countries including Namibia to apply evidence-based solutions. Primary data sources for small area
forecasts include population and housing censuses, surveys and administrative data. Although the
census provides a full count of the population as well as data on fertility, mortality and migration, these
operations are usually done only every ten years. National statistics offices typically generate projections
to produce inter-censual estimates; however, these are done at very coarse spatial scales. Hence,
making it challenging to capture demographic trends and patterns at finer geographic scales, especially
through migration. Mapping from satellites can capture the geographically uneven population growth
that is often difficult to predict and account for using standard projection models (Wardrop et al., 2018).
Likewise, mobile phone data offer opportunities to capture seasonal variation over time and space in
migration patterns. I am proposing a study that aims to assess how geospatial metrics can improve the
accuracy of population projection models. The study will explore different projection methods with
census 2011 as a baseline and use top-down models to disaggregate to small areas, and results will be
validated against 2021 Census data to identify the best estimates.