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

Abstract

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.

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

“…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.…”

Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa

“…2 and 5 ), as their accuracy appears to be highly dependent on the completeness of identified building structures. The quality of the underlying satellite data containing information on built environments and the applied methodology to automatically extract built features involves omission and commission errors, leading to an under- or overestimation of uninhabited areas 21 , 49 , 50 . While WorldPop top–down constrained uses polygon building footprint data and HRSL uses high resolution satellite imagery (~50 cm), GHS-POP extracts built features from Landsat 8 imagery with a resolution of ~30 meters 32 .…”

“…While WorldPop top–down constrained uses polygon building footprint data and HRSL uses high resolution satellite imagery (~50 cm), GHS-POP extracts built features from Landsat 8 imagery with a resolution of ~30 meters 32 . Due to the difficulty of detecting built-up areas from coarser resolution satellite imagery, GHS-POP and, to a lower extent, LandScan have previously been found to overestimate uninhabited zones and thus underestimate people in sparsely populated sub-urban and rural areas 21 , 51 , 52 . We found similar patterns in two rural areas in Garissa and Nakuru counties in Kenya, where divergent patterns of settlement detection between the gridded population products were seen (Supplementary Figs.…”

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways

“…Based on the strengths and weaknesses of the WorldPop dataset, combined with the comparative analysis results of the released global gridded population datasets (including GPW, GHS-POP, WorldPop, and LandScan) by Yin et al . 34 , and considering the problem of data time series, we decided to use the unconstrained global population grids as the population input data for this study.…”