GHS-POP Accuracy Assessment: Poland and Portugal Case Study

Całka, Beata; Bielecka, Elżbieta

doi:10.3390/rs12071105

Cited by 25 publications

(15 citation statements)

References 53 publications

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“…Large errors increase significantly when the population density and growth rate are higher than 610 persons/km 2 and 1.54% in this research, respectively. Similarly, another study also confirmed that the GHS-POP underestimates the population in densely populated regions [26]. Therefore, we appeal that the gridded population data producers would value highly the high population density and growth rate regions when they distribute the population.…”

Section: Suggestions For the Gridded Datasets' Producers And Userssupporting

confidence: 60%

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Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA)

Yin

Zhu

et al. 2021

IJGI

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The release of global gridded population datasets, including the Gridded Population of the World (GPW), Global Human Settlement Population Grid (GHS-POP), WorldPop, and LandScan, have greatly facilitated cross-comparison for ongoing research related to anthropogenic impacts. However, little attention is paid to the consistency and discrepancy of these gridded products in the regions with rapid changes in local population, e.g., Mainland Southeast Asia (MSEA), where the countries have experienced fast population growth since the 1950s. This awkward situation is unsurprisingly aggravated because of national scarce demographics and incomplete census counts, which further limits their appropriate usage. Thus, comparative analyses of them become the priority of their better application. Here, the consistency and discrepancy of the four common global gridded population datasets were cross-compared by combing the 2015 provincial population statistics (census and yearbooks) via error-comparison based statistical methods. The results showed that: (1) the LandScan performs the best both in spatial accuracy and estimated errors, then followed by the WorldPop, GHS-POP, and GPW in MSEA. (2) Provincial differences in estimated errors indicated that the LandScan better reveals the spatial pattern of population density in Thailand and Vietnam, while the WorldPop performs slightly better in Myanmar and Laos, and both fit well in Cambodia. (3) Substantial errors among the four gridded datasets normally occur in the provincial units with larger population density (over 610 persons/km2) and a rapid population growth rate (greater than 1.54%), respectively. The new findings in MSEA indicated that future usage of these datasets should pay attention to the estimated population in the areas characterized by high population density and rapid population growth.

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Section: Suggestions For the Gridded Datasets' Producers And Userssupporting

confidence: 60%

“…MSEA is experiencing rapid population changes with a high population growth rate and accelerating urbanization since the 1950s [25][26][27]. A two-decade (2000-2019) average of total population increment in Cambodia, Laos, Myanmar, Thailand, and Vietnam were 35.6%, 34.67%, 15.68%, 10.6%, and 20.71%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA)

Yin

Zhu

et al. 2021

IJGI

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“…In theory, similar to the accuracy assessment of any other RS thematic map, a comprehensive quantitative evaluation of population distribution grids should be based on independent and high-resolution ground-truth data, such as population numbers at the pixel level. However, due to the fact that these types of reference data hardly exist at large scales (e.g., they are only available for some countries) [21,56,57], or when they do exist are difficult to acquire due to privacy protection policies, a "true-validation" of continental and global gridded population distribution datasets is still not possible to implement.…”

Section: Quantitative Accuracy Assessmentmentioning

confidence: 99%

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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“…Because GHS-POP relies 265 on GHS-BUILT, for which detection in sparse and rural areas is lacking (Leyk et al, 2018), it may tend to overconcentrate population into built-up areas, overestimating the number of urban residents (depending on how urban areas themselves are delineated). GHS-POP has been shown in recent studies to produce the most accurate pixel-level population estimates when compared to local data for some locations, especially in urban areas (Archila Bustos et al, 2020;Calka and Bielecka, 2020). 270…”

Section: Ghs-pop 255mentioning

confidence: 99%

Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990–2015: How data choices matter

MacManus

Balk

Engin

et al. 2021

Preprint

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Abstract. The accurate estimation of population living in the Low Elevation Coastal Zone (LECZ), and at heightened risk from sea level rise, is critically important for policy makers and risk managers worldwide. This characterization of potential exposure depends not only on robust representations of coastal elevation and spatial population data, but also of settlements along the urban-rural continuum. The empirical basis for LECZ estimation has improved considerably in the 13 years since it was first estimated that 10 % of the world’s population, and an even greater share of the urban population, lived in the LECZ (McGranahan et al., 2007). Those estimates were constrained in several ways, most notably by a single 10-meter LECZ, but also by a dichotomous urban-rural proxy and population from a single source. This paper updates those initial estimates with newer, improved inputs and provides a range of estimates, along with sensitivity analyses that reveal the importance of understanding the strengths and weaknesses of the underlying data. We estimate that between 750 million to nearly 1.1 billion persons globally, in 2015, live in the ≤ 10 m LECZ, with the variation depending on the elevation and population data sources used. The variations are considerably greater at more disaggregated levels, when finer elevation bands (e.g. the ≤ 5 m LECZ) or differing delineations between urban, quasi-urban and rural populations are considered. Despite these variations, there is general agreement that the LECZ is disproportionately home to urban dwellers, and that the urban population in the LECZ has grown more than urban areas outside the LECZ since 1990. We describe the main results across these new elevation, population, and urban proxy data sources in order to guide future research and improvements to characterizing risk in low elevation coastal zones. DOI: assigned upon completion of data peer-review.

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GHS-POP Accuracy Assessment: Poland and Portugal Case Study

Cited by 25 publications

References 53 publications

Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA)

Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA)

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

Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990–2015: How data choices matter

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