Spatial identification of poverty in mountainous cities based on the mountain poverty spatial index: A case study of Ganzhou city in 2018 in China Spatial identification of poverty in mountainous cities based on the mountain poverty spatial index: A case study of Ganzhou city in 2018 in China

最小化 最大化

Vol19 No.11: 3213-3226 

Title】Spatial identification of poverty in mountainous cities based on the mountain poverty spatial index: A case study of Ganzhou city in 2018 in China

Author】WANG Jin-liang1,2; CUI Zhi-chao1; ZHOU Bing-juan1,2*

Addresses】1 School of Land Resources and Environment, Jiangxi Agriculture University, Nanchang 330045, China; 2 Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology of Jiangxi Province, Jiangxi Agriculture University, Nanchang 330045, China

Corresponding author】ZHOU Bing-juan

Citation】Wang JL, Cui ZC, Zhou BJ (2022) Spatial identification of poverty in mountainous cities based on the mountain poverty spatial index: A case study of Ganzhou city in 2018 in China. Journal of Mountain Science 19(11). https://doi.org/10.1007/s11629-022-7460-0

DOI】https://doi.org/10.1007/s11629-022-7460-0

Abstract】Poverty is a severe barrier to sustainable human development and a pressing worldwide issue. Understanding how to accurately assess the spatial distribution of poverty in mountain areas has become crucial for ensuring that governments at all levels take suitable poverty reduction strategies. In this study, the mountain poverty spatial index (MPSI) was created by combining the digital elevation model (DEM), Luojia-1 night-time light imagery, point of interest (POI) data, and vegetation index products. The MPSI was then used to identify the spatial characteristics of poverty at different scales in the hilly area of Ganzhou city, Jiangxi Province, China. Socioeconomic statistics and Google satellite images were used to verify the reliability of MPSI by constructing a multidimensional poverty index (MPI) at the county scale. The results showed that MPSI and MPI have a positive correlation with a correlation coefficient of 0.8934 (P<0.001), which indicates that MPSI could be used to identify the spatial distribution of poverty well. Specifically, the smallest distribution of both MPSI and MPI was in Zhanggong District (1.4555 and 0.1894), which indicates that most of the affluent counties were concentrated in the central region of Ganzhou, and the poor areas were scattered in the surrounding areas of Ganzhou. In addition, MPSI accurately identified poverty in mountainous areas with complex terrain in small administrative units, which can provide a more accurate way to monitor the poverty situation in the mountainous areas of China.This study will be useful for providing scientific references for the Chinese government to implement targeted strategies for eradicating poverty with differentiated policies.

Keywords】Mountain poverty; Night-time light; Remote sensing imagery; Vegetation index; Point of interest; Synthetic human settlement index