Multiscale relative poverty assessment based on multi-source remote sensing and POI data——A case study of rocky desertification area in Yunnan,Guangxi and Guizhou
In 2020,China eliminated absolute poverty and achieved comprehensive poverty alleviation,but the problem of relative poverty continues to exist for a long time.The development of contiguous poverty-stricken areas and the management of relative poverty were the keys to achieving common prosperity for the country.Based on multiple data sources,this paper analyzed the problem of rela-tive poverty from multiple perspectives.Firstly,multi-source data such as night light,land use,DEM,NDVI,socio-economic statistics and points of interest(POI)were combined to construct a multi-dimensional(population,social,economic,resource,ecological and disaster)development index model(CMDI)to quantitatively identify relatively poor counties.Then,the Moran index and spatial local autocorrelation were used to study the spatiotemporal dynamic differences of relative poverty levels.Finally,the spatial distribution of relative poverty at the grid scale was constructed.The results showed that:The relative poverty status of different dimensions in the rocky desertification area of Guangxi in 2021 was identified based on CMDI,among which the multi-dimensional extremely poor coun-ties,poor counties and poorer counties accounted for 5.7％,20％and 22.9％,respectively.In 2011,most of the counties in the rocky desertification area of Yunnan,Guangxi and Guizhou belonged to extremely poor areas and poverty-stricken areas,of which 55％were extremely poor areas and 34％were poverty-stricken areas.The multidimensional relative poverty degree in the rocky desertification area of Yunnan,Guangxi and Guizhou showed a significant spatial positive correlation.The spatial distribution of relative poverty at the grid scale can effectively showed the spatio-temporal evolution of poverty in the study area.
night lighting dataPOImultidimensional relative povertyspatio-temporal evolutionrocky desertification area
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