Implications for Tracking SDG Indicator Metrics with Gridded Population Data

Tuholske, Cascade; Gaughan, Andrea E.; Sorichetta, Alessandro; Sherbinin, Alex de; Bucherie, Agathe; Hultquist, Carolynne; Stevens, Forrest R.; Kruczkiewicz, Andrew; Huyck, Charles; Yetman, G.

doi:10.3390/su13137329

Cited by 17 publications

(13 citation statements)

References 48 publications

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“…This analysis reinforces findings of other studies which find that currently available gridded population products tend to underestimate populations in urban areas [ 94 – 96 ], especially in higher-density poorer neighbourhoods [ 97 ]. For example, Tuholske and colleagues (2021) compared five gridded population products to estimate the proportion of population affected by natural disasters (SDG 11.5) in three regions where disasters had occurred, and found that 1x1km population estimates varied widely among data products, and reflected anywhere from 20% to 80% of the total UN estimated population in each region.…”

Section: Discussionsupporting

confidence: 89%

“…For example, Tuholske and colleagues (2021) compared five gridded population products to estimate the proportion of population affected by natural disasters (SDG 11.5) in three regions where disasters had occurred, and found that 1x1km population estimates varied widely among data products, and reflected anywhere from 20% to 80% of the total UN estimated population in each region. Furthermore, they found that WorldPop-Global-Unconstrained generally performed better than un-modelled products (e.g., GPW), but not as well as products that constrained estimates to settled cells (e.g., GHS-POP) [ 94 ]. In a separate comparison of nine gridded population estimates of Kenyan and Nigerian slum populations (SDG 11.1.1) where field counts were available for reference, the estimated population in each slum varied widely and WorldPop-Global-Unconstrained estimates reflected just 11% of the overall slum population while the best performing data product (HRSL) estimated just 34% of all slum dwellers [ 97 ].…”

Section: Discussionmentioning

confidence: 99%

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How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

Thomson

Leasure²,

Bird³

et al. 2022

PLoS ONE

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Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

Thomson

Leasure²,

Bird³

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…This analysis reinforces findings of other studies which find that currently available gridded population products tend to underestimate populations in urban areas [94][95][96], especially in higher-density poorer neighbourhoods [97]. For example, Tuholske and colleagues (2021) compared five gridded population products to estimate the proportion of population affected by natural disasters (SDG 11.5) in three regions where disasters had occurred, and found that 1x1km population estimates varied widely among data products, and reflected anywhere from 20% to 80% of the total UN estimated population in each region.…”

Section: Plos Onesupporting

confidence: 90%

How Accurate are WorldPop-Global-Unconstrained Gridded Population Data at the Cell-Level?: A Simulation Analysis in Urban Namibia

Thomson¹,

Leasure²,

Bird³

et al. 2022

Preprint

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Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners. These datasets are evaluated for accuracy at the spatial scale of the input data which is often much courser (e.g. administrative units) than the neighbourhood or cell-level scale of many applications. We simulate a realistic "true" 2016 population in Khomas, Namibia, a majority urban region, and introduce realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate these simulated realistic populations by census and administrative boundaries (to mimic census data), and generate 32 gridded population datasets that are typical of a LMIC setting using WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these simulated datasets using the original "true" population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells, driven by the use of average population densities in large areal units to determine cell-level population densities. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy. It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales with relevance to small dense deprived areas within larger administrative units.

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“…The potential uncertainty due to regional exposure data has not been thoroughly studied. There are a few exceptions for flood modeling, including: Englehardt et al (2019), which examines the effect that refined estimates of building exposure have on flood loss estimates based on the assignment of vulnerability and its sensitivity to the designation of ''urban'' and ''rural'' in developing countries, and an examination of using various global population data sets, as with Lim et al (2019) and Tuholske et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Characterizing uncertainty of general building stock exposure data

Huyck

Eguchi

et al. 2022

Earthquake Spectra

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Effective disaster risk management (DRM) and disaster risk reduction (DRR) require modeling potential and post-event impacts using building exposure data. The data used to develop building exposure databases will influence the accuracy of risk assessments and the appropriateness of subsequent decisions. This article proposes a framework for classifying approaches of developing building exposure databases into levels. To examine the uncertainty introduced through using various approaches to exposure development, a probabilistic seismic risk assessment was run with the exposure data corresponding to each proposed level using the County of Los Angeles as the study area. A factor of ∼2.5 was observed in the final loss estimates. The variance was less dependent on the spatial scale of data than on key values, most notably estimates of building size and replacement cost.

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Implications for Tracking SDG Indicator Metrics with Gridded Population Data

Cited by 17 publications

References 48 publications

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

How Accurate are WorldPop-Global-Unconstrained Gridded Population Data at the Cell-Level?: A Simulation Analysis in Urban Namibia

Characterizing uncertainty of general building stock exposure data

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