Improved Estimates of Population Exposure in Low-Elevation Coastal Zones of China.

Yang Yang,Ye Ye,Chen Chen,Jin Jin,Yao Yao

doi:10.3390/ijerph16204012

Abstract

With sea level predicted to rise and the frequency and intensity of coastal flooding expected to increase due to climate change, high-resolution gridded population datasets have been extensively used to estimate the size of vulnerable populations in low-elevation coastal zones (LECZ). China is the most populous country, and populations in its LECZ grew rapidly due to urbanization and remarkable economic growth in coastal areas. In assessing the potential impacts of coastal hazards, the spatial distribution of population exposure in China’s LECZ should be examined. In this study, we propose a combination of multisource remote sensing images, point-of-interest data, and machine learning methods to improve the performance of population disaggregation in coastal China. The resulting population grid map of coastal China for the reference year 2010, with a spatial resolution of 100 × 100 m, is presented and validated. Then, we analyze the distribution of population in LECZ by overlaying the new gridded population data and LECZ footprints. Results showed that the total population exposed in China’s LECZ in 2010 was 158.2 million (random forest prediction) and 160.6 million (Cubist prediction), which account for 12.17% and 12.36% of the national population, respectively. This study also showed the considerable potential in combining geospatial big data for high-resolution population estimation.

Highlights

Coastal areas are associated with large and growing concentrations of human population and socioeconomic activities, including many large cities of the world [1]
Accurate gridded population dataset for China, the WorldPop dataset was summed at the Jiedao/Xiangzhen level to population dataset for China, the WorldPop dataset was summed at the Jiedao/Xiangzhen level compare the accuracy of the three population datasets
We selected the measures of mean relative (MRE), mean absolute deviation (MAE), and root mean squared error (MSE) (RMSE) to compare and analyze the errors of error (MRE), mean absolute deviation (MAE), and root MSE (RMSE) to compare and analyze the the above population dataset

Summary

Introduction

Coastal areas are associated with large and growing concentrations of human population and socioeconomic activities, including many large cities of the world [1]. According to estimates from the Global Rural Urban Mapping Project (GRUMP) gridded population dataset for 2000, this zone covers 2% (2.7 million km2 ) of the world’s land area but contains 10% (634 million) of the world’s total population [3]. According to the LandScan population dataset (https://landscan.ornl.gov/), 690 million people in 2006 [4] and 726 million in 2008 lived in the global LECZ [5]. These studies showed that inherent uncertainties of the input datasets and methods will likely affect conclusions, and variations in results were highly dependent on the input datasets [4,5]. Most studies used gridded population datasets with a spatial resolution of 1 × 1 km, which captures more area than finer resolution, thereby overestimating the LECZ land area and population relative to finer grid [6]

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Methods

Discussion

Conclusion