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

Abstract

The field of human population mapping is constantly evolving, leveraging the increasing availability of high-resolution satellite imagery and the advancements in the field of machine learning. In recent years, the emergence of global built-area datasets that accurately describe the extent, location, and characteristics of human settlements has facilitated the production of new population grids, with improved quality, accuracy, and spatial resolution. In this research, we explore the capabilities of the novel World Settlement Footprint 2019 Imperviousness layer (WSF2019-Imp), as a single proxy in the production of a new high-resolution population distribution dataset for all of Africa—the WSF2019-Population dataset (WSF2019-Pop). Results of a comprehensive qualitative and quantitative assessment indicate that the WSF2019-Imp layer has the potential to overcome the complexities and limitations of top-down binary and multi-layer approaches of large-scale population mapping, by delivering a weighting framework which is spatially consistent and free of applicability restrictions. The increased thematic detail and spatial resolution (~10m at the Equator) of the WSF2019-Imp layer improve the spatial distribution of populations at local scales, where fully built-up settlement pixels are clearly differentiated from settlement pixels that share a proportion of their area with green spaces, such as parks or gardens. Overall, eighty percent of the African countries reported estimation accuracies with percentage mean absolute errors between ~15% and ~32%, and 50% of the validation units in more than half of the countries reported relative errors below 20%. Here, the remaining lack of information on the vertical dimension and the functional characterisation of the built-up environment are still remaining limitations affecting the quality and accuracy of the final population datasets.

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

“…Broad-scale (i.e., global and continental) satellite-driven human settlement products, including the Global Human Settlement Layer (GHSL; [1,2]), the World Settlement Footprint (WSF; [3]), the High Resolution Settlement Layer (HRSL; [4]) and Geo-Referenced Infrastructure and Demographic Data for Development settlement extent data (GRID3-SE; [5]) seek to represent the presence and extent of populated built-up regions across an array of urban and rural geographies [6]. These products have broadened our awareness of where humans live and work [7][8][9] and have made important contributions to population modeling [10][11][12][13][14], development monitoring [8,[15][16][17] and climate hazard mitigation [18][19][20][21].…”

From auxiliary data to research prospects, a review of gridded population mapping

“…The errors include three categories: relative error (RE), mean square error (MSE), and residuals (R). They can be subdivided into relative error (RE) (Harvey, 2002; Palacios‐Lopez et al, 2021; Schug et al, 2021), mean relative error (MRE) (Harvey, 2002; Lo, 1995; Palacios‐Lopez et al, 2021; Schug et al, 2021), median relative error (MdRE) (Dmowska & Stepinski, 2017; Harvey, 2002), root mean square error (RMSE) (Eicher & Brewer, 2001; Palacios‐Lopez et al, 2021; Schug et al, 2021; Sorichetta et al, 2015), mean square error (MSE) (Li et al, 2020; You et al, 2021), relative root mean square error (%RMSE) (Dmowska & Stepinski, 2017; Stevens et al, 2015), residual (R) (Calka & Bielecka, 2019; Li et al, 2020), mean residual (MR) (Palacios‐Lopez et al, 2022; Schug et al, 2021; Sorichetta et al, 2015), and median residual (MdR) (Robinson et al, 2017). The median and mean values of the above errors are calculated based on absolute values.…”

Population spatialization at building scale based on residential population index—A case study of Qingdao city

“…Population distribution data, as the basic data that reflects social and development situation of a country or city [ 1 , 2 ], is also one of the most important basic data in social and geographical research, being widely used in urban planning, social resource allocation, environmental protection and other fields [ 3 , 4 ]. The existing population distribution data are largely dependent on demographic data, which are often collected step-by-stepwith administrative divisions as units, which not only take a long time to update and consume a lot of manpower, but also have low spatial and temporal resolution, making it difficult to express spatial distribution details.…”