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

Xuchao Yang, Chenming Yao, Qian Chen, Tingting Ye and Cheng Jin
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Xuchao Yang: Ocean College, Zhejiang University, Zhoushan 310027, China
Chenming Yao: Ocean College, Zhejiang University, Zhoushan 310027, China
Qian Chen: Ocean College, Zhejiang University, Zhoushan 310027, China
Tingting Ye: Ocean College, Zhejiang University, Zhoushan 310027, China
Cheng Jin: Ocean College, Zhejiang University, Zhoushan 310027, China

IJERPH, 2019, vol. 16, issue 20, 1-15

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.

Keywords: LECZ; population exposure; random forest; Cubist; point-of-interest (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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