  About half of the world population already live in urban areas. It is
projected that by 2050, approximately 70% of the world population will live in
cities. In addition to this, most developing countries do not have reliable
population census figures, and periodic population censuses are extremely
resource expensive. In Africa's most populous country, Nigeria, for instance,
the last decennial census was conducted in 2006. The relevance of near-accurate
population figures at the local levels cannot be overemphasized for a broad
range of applications by government agencies and non-governmental
organizations, including the planning and delivery of services, estimating
populations at risk of hazards or infectious diseases, and disaster relief
operations. Using GRID3 (Geo-Referenced Infrastructure and Demographic Data for
Development) high-resolution spatially disaggregated population data estimates,
this study proposed a framework for aggregating population figures at micro
levels within a larger geographic jurisdiction. Python, QGIS, and machine
learning techniques were used for data visualization, spatial analysis, and
zonal statistics. Lagos Island, Nigeria was used as a case study to demonstrate
how to obtain a more precise population estimate at the lowest administrative
jurisdiction and eliminate ambiguity caused by antithetical parameters in the
calculations. We also demonstrated how the framework can be used as a benchmark
for estimating the carrying capacities of urban basic services like healthcare,
housing, sanitary facilities, education, water etc. The proposed framework
would help urban planners and government agencies to plan and manage cities
better using more accurate data.
