Abstract
Population is one of the core elements of sustainable development. Quantifying the estimation accuracy of population spatial distribution has been recognized as a critical and challenging task. This study aims to evaluate the data accuracy of four population datasets in China, including three global gridded population datasets, the Gridded Population of the World (GPW), Global Rural and Urban Mapping Project (GRUMP), and WorldPop project (WorldPop), and a Chinese regional gridded population dataset, the China 1 km Gridded Population (CnPop) dataset. These datasets are assessed using a specific method based on a GIS-linked 2000 census dataset at the township level in China. The results indicate that WorldPop had the highest estimation accuracy, estimating about 60% of the total population. CnPop accurately estimated about half of the total population, showing a good mapping performance. The GPW had an acceptable estimation accuracy in a few plain and basin areas, accounting for about 30% of the total population. Compared to the GPW, GRUMP accurately estimated about 40% of the total population. The relative estimation error analysis discovered the disadvantages of the generation strategies of these datasets. The conclusions are expected to serve as a quality reference for potential dataset users and producers, and promote accuracy assessment for population datasets in other regions and globally.
Highlights
The world population has increased dramatically from 1.6 billion in 1900 to 7.6 billion in mid-2017, and 59.66% of those people live in Asia [1,2]
The objective of this study is to gain a comprehensive quantitative understanding of the overall quality and accuracy of China km Gridded Population (CnPop), Gridded Population of the World (GPW), Global Rural and Urban Mapping Project (GRUMP), and WorldPop in China, which can be further used as a quality reference for relevant research and applications
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Summary
The world population has increased dramatically from 1.6 billion in 1900 to 7.6 billion in mid-2017, and 59.66% of those people live in Asia [1,2]. The issue of population is one of the biggest problems in creating a sustainable society today. Adequate knowledge of population distributions has proven to be essential in many domains, such as environmental impact assessments, disaster prevention and mitigation, medical treatments, regional sustainable development, and climate change evaluations [3,4,5,6]. Available information on population distribution and composition largely relies on demographical data, generally counted using census tracts, blocks, postcode zones, townships, and villages. In statistical data, population density in an administrative unit is a single value; it does not reflect spatial distribution and internal variation [7]. Research based on statistical population data at different scales may significantly affect spatial patterns and associations; this is known as the modifiable areal unit problem. There is a need to convert the spatial resolution and structure of a statistical population to facilitate data linkage and analyses
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