Data Uncertainties in China’s Population

Jiang, Quanbao; Li, Xiaomin; Sánchez-Barricarte, Jesús J.

doi:10.5539/ass.v11n13p200

“…After a full-text review of these articles, we found eight additional reported census under-counts. Together, census under-counts in LMICs were collated from 10 PESs [45][46][47][48][49][50][51][52][53][54], and 13 articles [10,[55][56][57][58][59][60][61][62][63][64][65][66] (Fig 4). The average census under-counts were 46% in urban-slum populations, 6% in urban-non-slum populations, and 7% in rural populations (Table 2, see Supplement 3 for details).…”

Section: Plos Onementioning

confidence: 99%

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 Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale.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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“…To identify realistic levels of under-counts among urban-slum, urban-non-slum, and rural populations in LMIC censuses, we reviewed the scientific and grey literature. [7,[55][56][57][58][59][60][61][62][63][64][65][66] (Figure 4). The average census under-counts were 46% in urban-slum populations, 6% in urban-non-slum populations, and 7% in rural populations (Table 2, see Supplement 3 for details).…”

Section: Simulating Realistic Levels Of Under-count Inaccuracy In Censusesmentioning

confidence: 99%

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

Thomson¹,

Leasure²,

Bird³

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…After a full-text review of these articles, we found that eight additional articles reported census under-counts. Together, census undercounts in LMICs were collated from 10 PESs [43][44][45][46][47][48][49][50][51][52], and 13 articles [7,[53][54][55][56][57][58][59][60][61][62][63][64] (Figure 4). The average census under-counts were 46% in urban-slum populations, 6% in urban-non-slum populations, and 7% in rural populations ( Table 1, see Supplement 3 for details).…”

Section: Simulating Realistic Levels Of Under-count Inaccuracy In Cenmentioning

confidence: 99%

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

Dr¹,

Leasure²,

Bird³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

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, none of these datasets have been evaluated for accuracy of population estimates at the grid cell-level. This is because the finest-scale population figures generally available to data producers are those input into gridded population models and disaggregated to smaller grid cells (e.g., 100x100m). 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 then 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's gridded population approach. We evaluate the cell-level accuracy of these simulated WorldPop-Global datasets, using the original "true" population as a reference. In our simulation, we found large cell-level errors, particularly in urban cells, driven by WorldPop-Global's use of average population densities in large areal units to determine cell-level population densities. Age, accuracy, and aggregation of the input data did play a primary role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global in particular (e.g., from simulated populations, routine household surveys, or slum community profiles), and use of new building footprint datasets as a covariate to improve cell-level accuracy of gridded population data. It is important to measure cell-level accuracy of all gridded population datasets, especially if they are to be used for monitoring key development indicators.

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Data Uncertainties in China’s Population

Cited by 4 publications

References 21 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

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

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