  Over a billion people live in slums in settlements that are often located in
ecologically sensitive areas and hence highly vulnerable. This is a problem in
many parts of the world, but it is more prominent in low-income countries,
where in 2014 on average 65% of the urban population lived in slums. As a
result, building resilient communities requires quantifying the population
living in these deprived areas and improving their living conditions. However,
most of the data about slums comes from census data, which is only available at
aggregate levels and often excludes these settlements. Consequently,
researchers have looked at alternative approaches. These approaches, however,
commonly rely on expensive high-resolution satellite imagery and field-surveys,
which hinders their large-scale applicability. In this paper, we investigate a
cost-effective methodology to estimate the slum population by assessing the
quality of gridded population data. We evaluate the accuracy of the WorldPOP
and LandScan population layers against ground-truth data composed of 1,703
georeferenced polygons that were mapped as deprived areas and which had their
population surveyed during the 2010 Brazilian census. While the LandScan data
did not produce satisfactory results for most polygons, the WorldPOP estimates
were less than 20% off for 67% of the polygons and the overall error for the
totality of the studied area was only -5.9%. This small error margin
demonstrates that population layers with a resolution of at least a 100m, such
as WorldPOP's, can be useful tools to estimate the population living in slums.
