Measuring the accuracy of gridded human population density surfaces: a case study in Bioko Island, Equatorial Guinea

Abstract

Geospatial datasets of population are becoming more common in models used for health policy. Publicly-available maps of human population in sub-Saharan Africa make a consistent picture from inconsistent census data, and the techniques they use to impute data makes each population map unique. Each mapping model explains its methods, but it can be difficult to know which map is appropriate for which policy work. Gold-standard census datasets, where available, are a unique opportunity to characterize maps by comparing them with truth. We use census data from Bioko Island, in Equatorial Guinea, to evaluate LandScan (LS), WorldPop (WP), and the High-Resolution Settlement Layer (HRSL). Each layer is compared to the goldstandard using statistical measures to evaluate distribution, error, and bias. We investigated how map choice affects burden estimates from a malaria prevalence model. Specific population layers were able to match the gold-standard distribution at different population densities. LandScan was able to most accurately capture highly urban distribution, HRSL matched best at all other lower population densities and WorldPop performed poorly everywhere. Correctly capturing empty pixels is key, and smaller pixel sizes (100 m vs 1 km) improve this. Normalizing areas based on known district populations increased performance. The use of differing population layers in a malaria model showed a disparity in results around transition points between endemicity levels. The metrics in this paper, some of them novel in this context, characterize how these population maps differ from the gold standard census and from each other. We show that the metrics help understand the performance of a population map within a malaria model. The closest match to the census data would combine LandScan within urban areas and the HRSL for rural areas. Researchers should prefer particular maps if health calculations have a strong dependency on knowing where people are not, or if it is important to categorize variation in density within a city. population health | demographics | epidemiology | mapping

High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent

“…This was highlighted in the reviews presented by Kavvada et al [4], Kuffer et al [7], and Qui et al [10], where the authors argue that geospatial data related to human population distributions could potentially be used to directly or indirectly support, implement and monitor more than half of the SDGs (~11 out of 17 SDGs) and a large proportion of their related indicators (~98 of the 231 Indicators). Research in the fields of public security [11], health policy [12][13][14], network and transportation [15], vulnerability and risk assessment [16][17][18], urban growth [19] and mitigation [20] among others, are examples of the many areas where these datasets are needed as inputs to produce reliable information.…”