Poverty Identification And Spatio-temporal Identification In China Based On Nighttime Light Data

Poverty alleviation and poverty eradication have always been issues that have attracted the attention of all countries in the world.Realizing poverty alleviation and reducing the gap between urban and rural areas is one of the main goals for sustainable development of all countries.In the process of solving the poverty problem,the identification of the poor is the primary task for poverty alleviation.Only through more accurate identification of poverty can we avoid issues such as ambiguous targets,unclear goals,and poor performance in poverty alleviation efforts,so as to better help the poor and achieve poverty alleviation.Therefore,the measurement and identification of poverty targets have important reference value and significance for the government in poverty alleviation work and the formulation of poverty alleviation policies and regional development policies.Traditional poverty identification is mostly based on statistical data and socio-economic development data.It consumes a lot of manpower,material and financial resources,and lacks spatial information to meet the needs of a wide range of dynamic poverty monitoring.This study introduces nighttime remote sensing images and conducts research on county poverty in 2005-2015 in a relatively large spatial range.Through the coupling of statistical data and nighttime light data,and a variety of light indexes were constructed based on population and land use data.Select rural residents' net income as a measure of poverty,couple with the light characteristics index,and construct an income poverty estimation model.It is found that the goodness of fit is better based on the light index per unit construction land,the unit population light index and the per capita net income of farmers.In addition,based on the SLA method,this study constructed a multi-dimensional poverty measurement index system for China's counties,and obtained a multidimensional poverty index CPI.Based on the light characteristics indexes and CPI index,a multidimensional poverty estimation model was established.After comparison,the average light index and the CPI index were found to be similar.The degree of convergence is better.The two models were tested in the Yellow River Beach of Henan Province,and he results showed that the average relative errors of income poverty estimation models in 2005 and 2015 were 21.78% and 14.76% respectively,and the average errors of multidimensional poverty estimation models were respectively 21.89% and 13.58.%The comparison shows that the two models have similar degrees of similarity,but the acquisition and calculation of the statistical data involved in the income poverty measurement model is relatively troublesome.Therefore,under the same conditions,this study selected a multidimensional poverty estimation model to measure the poverty in counties across the country,and obtained the spatial distribution map of poverty in China's counties from 2005 to 2015,and ranked them.At the same time,the 2011 CPI index map based on the model was overlaid with the national designated poverty alleviation and development key counties and found that the two are basically consistent,so the model can be used to estimate the distribution of poverty in China.Through relatively long-term comparisons,China's poverty-stricken areas are mostly distributed on the northwest side of the Hu Huanyong Line,and the distribution has been relatively stable.Therefore,this study considers that the natural environment is an important influencing factors for regional poverty.In addition,this study uses spatial exploratory data analysis methods to analyze the spatial agglomeration characteristics of multidimensional poverty in China's counties.The study finds that in 2005 and 2015,the Moran's I index of poverty distribution in the counties and regions in China was 0.476 and 0.395.The measured values of Geary's C index were 0.00094 and 0.00066,respectively,showing a clear positive spatial correlation,indicating that the distribution of the poverty-stricken areas in China shows a significant spatial agglomeration on the county scale.The result also shows that from 2005 to 2015,the CPI index in the eastern coastal areas of China has been high,and the spatial distribution characteristics of high-high agglomeration have been shown,indicating that the level of social and economic development in the eastern part of China has always maintained a leading position;the CPI in the southwest and northwest regions is generally low,with a low-low distribution trend,and the overall development is still in a disadvantageous position;there is no obvious high-high or high-low agglomeration distribution characteristics in the central region;the distribution of poverty in Xinjiang and Sichuan Province has the most obvious change,which evolved from the low-low accumulation in 2005 to the high-high agglomeration distribution in 2015,this reflects the remarkable achievements in socio-economic development and poverty alleviation in these areas over the past decade.