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

Xu Yin,Chiwei Xiao,Yanzhao Yang,Zhiming Feng,Zhen You,Peng Li

doi:10.3390/ijgi10100681

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.

Highlights

Global gridded population datasets have become one of the most essential inputs for quantifying the impacts of human beings on the Earth and understanding the humannature interrelationship in the face of climate change, disaster risk and epidemic spreading [1,2,3]
It may have a close relation to the spillover effect of night time light (NTL) as auxiliary input data of the WorldPop [7,10]
For the sake of better understanding the discrepancy of the existing gridded population datasets, this study provides a reference for similar cross-comparative analysis in other countries, especially the densely populated ones, such as Indonesia, India, and Mexico

Summary

Introduction

Global gridded population datasets have become one of the most essential inputs for quantifying the impacts of human beings on the Earth and understanding the humannature interrelationship in the face of climate change, disaster risk and epidemic spreading [1,2,3]. The booming population puts increasingly intense pressure on the Earth, and requires timely and updated accurate demographics for different purposes at various scales. Ever since the first release of the Gridded Population of World (GPW) version 1.0 in. 1995 [7], several continental to global gridded population datasets were successively publicized. Four of them are commonly applied in well-known academic journals and thematic reports or books, including the GPW [8], the Global Human Settlement

Methods

Results

Discussion

Conclusion