Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South

Stevens, Forrest R.; Gaughan, Andrea E.; Nieves, Jeremiah J.; King, Adam; Sorichetta, Alessandro; Linard, Catherine; Tatem, Andrew J.

doi:10.1080/17538947.2019.1633424

Cited by 38 publications

(39 citation statements)

References 43 publications

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“…To produce the population distribution maps needed for validation, we first generated the aggregated version of the L1-units, following a sampling and merging methodology similar to that employed by Stevens et al [43]. For each country, we started by randomly selecting one third of the L1-units.…”

Section: Random Samplingmentioning

confidence: 99%

“…Representative examples include recent population distribution datasets that have been produced on the basis of the World Settlement Footprint 2015 products (WSF2015 and WSF2015-Density) [32]; the new WorldPop Sub-Saharan gridded building datasets [33][34][35]; or through the joint analysis of high-resolution binary built-area products [36,37], such as the Global Urban Footprint [38,39], the High Resolution Settlement Layer [23,40] and the Global Human Settlement Layer [41,42], respectively. Here, the particular focus placed on built-area datasets for population modelling arises from the fact that different research has demonstrated that when built-area datasets are used to restrict the distribution of the population, the final products deliver better qualitative and quantitative results in comparison to those models where the datasets are not included [37,43]. In fact, other research has shown that when a given built-area dataset is accurate and coherent enough with population densities, it has the potential to be used as a single proxy for population modelling [43].…”

Section: Introductionmentioning

confidence: 99%

“…Here, the particular focus placed on built-area datasets for population modelling arises from the fact that different research has demonstrated that when built-area datasets are used to restrict the distribution of the population, the final products deliver better qualitative and quantitative results in comparison to those models where the datasets are not included [37,43]. In fact, other research has shown that when a given built-area dataset is accurate and coherent enough with population densities, it has the potential to be used as a single proxy for population modelling [43].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Random Samplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…It also addresses the notoriously flawed administrative boundaries, especially in proximity of coastal areas. Stevens et al (2019) provide insights on how the available built-up information products affect the ability to predict population density and distributions. The paper stresses that builtup data are very often the most important predictor of populated places and better insights in their accuracies may help in improving population density grids.…”

Section: Papers Of This Special Issuementioning

confidence: 99%

Measuring and understanding global human settlements patterns and processes: innovation, progress and application

Ehrlich

Balk²,

Sliuzas³

2019

International Journal of Digital Earth

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“…In these areas, the rapid influx of migrants can strain local health systems, food and water supplies, sanitation and waste management systems, as well as other public services such as education, and thus lead to a diverse set of environmental, social and health impacts [3,6]. It is therefore of crucial importance for policy makers to understand the spatial and temporal population growth patterns within their constituency for adequate resource allocation, development planning or disaster management [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning

et al. 2020

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Studies on annual settlement growth have mainly focused on larger cities or incorporated data rarely available in, or applicable to, sparsely populated areas in sub-Saharan Africa, such as aerial photography or night-time light data. The aim of the present study is to quantify settlement growth in rural communities in Burkina Faso affected by industrial mining, which often experience substantial in-migration. A multi-annual training dataset was created using historic Google Earth imagery. Support vector machine classifiers were fitted on Landsat scenes to produce annual land use classification maps. Post-classification steps included visual quality assessments, majority voting of scenes of the same year and temporal consistency correction. Overall accuracy in the four studied scenes ranged between 58.5% and 95.1%. Arid conditions and limited availability of Google Earth imagery negatively affected classification accuracy. Humid study sites, where training data could be generated in proximity to the areas of interest, showed the highest classification accuracies. Overall, by relying solely on freely and globally available imagery, the proposed methodology is a promising approach for tracking fast-paced population dynamics in rural areas where population data is scarce. With the growing availability of longitudinal high-resolution imagery, including data from the Sentinel satellites, the potential applications of the methodology presented will further increase in the future.

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Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South

Cited by 38 publications

References 43 publications

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

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

Measuring and understanding global human settlements patterns and processes: innovation, progress and application

Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning

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