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.
Highlights
Population growth has placed certain pressures on society, resources, and the ecological environment, and even affected ecosystem functions [1,2]
IPnWSrvi4L. aInnkSariaLnadnkthaeaTnidbet Autonomous Region of China, the consistency between WorldPop and Gridded Population of the World version 4 (GPWv4) is the highest, and the consistency between WorldPop and Global Human Settlement Layer (GHSL) is 4–30% lower than that between WorldPop and GPWv4. This indicates that the portrayal of characteristics of population distribution varies depending on the spatialization methods in different case areas, and GHSL, which is integrated with built-up areas extracted from remote sensing, is more advantageous in areas with a high urbanization level (Table 6)
In order to understand differences in the number and spatial distribution of the main spatial population datasets in the world, four datasets with different spatiotemporal resolutions (HYDE, GPWv4, GHSL and WorldPop), developed based on multiple data sources and spatialization methods, were selected, and Sri Lanka, the UK, Argentina and the Tibet Autonomous Region of China were taken as the case areas
Summary
Population growth has placed certain pressures on society, resources, and the ecological environment, and even affected ecosystem functions [1,2]. Spatial population datasets shared at the global and regional scales include Gridded Population of the World (GPW) [11], Global Human Settlement Layer (GHSL) [12], History Database of the Global Environment (HYDE) [13,14,15], WorldPop [16,17], Global Urban Footprint (GUF), High-Resolution Settlement Layer (HRSL), and so on These data have been widely used in disaster assessment and risk management [18,19,20,21,22], land use change modeling [23,24,25,26], public health services [27,28,29], and ecological environment change [30,31,32,33,34] and socioeconomic analysis [35] as important references for developing new population spatial datasets [36,37,38]. Geo-Inf. 2020, 9, 637 estimation deviation [43], consistency of spatial population distribution [44], and population density level distribution at the administrative unit and pixel scale, so as to provide a reference for the selection of population datasets in socioeconomic or ecological environment research [41,45]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
