Study on Spatial Clustering and Correlation of Multidimensional Poverty

Regional multidimensional poverty is an important direction of China’ s poverty reduction after 2020, and also an important content of theoretical research on poverty reduction. Based on the 2010, 2012, 2014, 2016 and 2018, the related data of 31 provinces, cities and autonomous regions nationwide, using principal component analysis, system cluster analysis and Moran’ s I index analysis method to study the various provincial regional spatial clustering of multidimensional poverty and its correlation, in order to analyze our country regional spatial characteristic and regional difference of multidimensional poverty. The results show that there are differences in the degree of multi-dimensional poverty in various regions of China, among which Beijing and other regions have gradually got rid of multi-dimensional poverty, Tibet and other regions have serious multi-dimensional poverty, and other regions have shown multi-dimensional poverty in different degrees. At the same time, there is a spatial autocorrelation between the multidimensional poverty in different regions. On this basis, the paper puts forward some Suggestions on spatial and multidimensional poverty management.