Multiple Global Population Datasets: Differences and Spatial Distribution Characteristics

Abstract

Spatial data of regional populations are indispensable in studying the impact of human activities on resource utilization and the ecological environment. Because the differences between datasets and their spatial distribution are still unclear, this has become a puzzle in data selection and application. This study is based on four mainstream spatialized population datasets: the History Database of the Global Environment version 3.2.000 (HYDE), Gridded Population of the World version 4 (GPWv4), Global Human Settlement Layer (GHSL), and WorldPop. In view of possible influences of geographical factors, this study analyzes the differences in accuracy of population estimation by computing relative errors and population spatial distribution consistency in different regions by comparing datasets pixel by pixel. The results demonstrate the following: (1) Source data, spatialization methods, and case area features affect the precision of datasets. As the main data source is statistical data and the spatialization method maintains the population in the administrative region, the populations of GPWv4 and GHSL are closest to the statistical data value. (2) The application of remote sensing, mobile communication, and other geospatial data makes the datasets more accurate in the United Kingdom, with rich information, and the absolute value of relative errors is less than 4%. In the Tibet Autonomous Region of China, where data are hard to obtain, the four datasets have larger relative errors. However, the area where the four datasets are completely consistent is as high as 84.73% in Tibet, while in the UK it is only 66.76%. (3) The areas where the spatial patterns of the four datasets are completely consistent are mainly distributed in areas with low population density, or with developed urbanization and concentrated population distribution. Areas where the datasets have poor consistency are mainly distributed in medium population density areas with high urbanization levels. Therefore, in such areas, a more careful assessment should be made during the data application process, and more emphasis should be placed on improving data accuracy when using spatialization methods.

The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets

“…Grid3 is an emerging dataset that presently provides population data for several African countries as a bottom-up model. The historic HYDE model, based on the United Nation's World Population Prospects and historical estimations from the literature [23,24], provides a time series of the human population with a spatial resolution of 10 km. These population models use a variety of ancillary data (e.g., built-up masks, land cover, land use, roads, infrastructure, services, night-time lights, topography and points of interest [7,9,25]) that assist in the spatial distribution of the population [8,25,26].…”

“…Commonly used methods include the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) [9,39]. Table 2 summarizes five common causes of errors: the spatial heterogeneity of the environment (e.g., variations in population densities) [23,40]; the quality of census data (e.g., temporal and spatial resolution) [32,41]; the quality of ancillary data [42] (e.g., reliance on coarse-resolution night-time light data); the scale effect and temporal mismatch increase uncertainty (e.g., differences in data availabilities) [8]; and differences in regional and local characteristics (e.g., differences in the urbanization rate). Most HIC countries have a relatively slow rate of population growth and a more stable settlement pattern than LMICs, impacted by uncontrolled urbanization (e.g., slums).…”

“…Strengths and weaknesses of population models reported in the literature[8,23,33,34,[36][37][38] (GPWv4 is based on 2010 round of census and includes over 12,500,000 units and GPWV3 was based on 2000 round of census and includes over 375,000 input units).…”

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

“…Then, it would not be surprising that researchers make informed decisions according to their needs. Usually, empirical research at the country level collects and utilizes population counts from census and/or yearbooks as actual value to assess the accuracy of gridded population datasets [18,[21][22][23][24]. For example, Bai et al [21] evaluated the data accuracy of the GPW, Global Rural-Urban Mapping Project (GRUMP), Worldpop, and China 1 km Gridded Population (CnPop) datasets in China, and found that the Worldpop had the highest estimation accuracy.…”

Small area population denominators for improved disease surveillance and response

“…This is often a challenge to assess, as where detailed, recent population data exist, often it is the input to the models, leaving a lack of independent data to compare against. Studies using detailed census data ( Bustos et al, 2020 , Chen et al, 2020 , Fries et al, 2021 , Yin et al, 2021 ), as well as cross-validation ( Reed et al, 2018 , Stevens et al, 2015 , Stevens et al, 2020 ) have tried to assess how well different models replicate population numbers and distributions at the scale of available data, and unsurprisingly those more complex models using detailed settlement mapping and range of covariates tend to do best. However, multiple trade-offs exist in the production of such datasets that depend on aspects such as input data availability, geographical extent, temporal range, spatial resolution, intended use and user needs.…”