The Temporal and Spatial Analysis of County Poverty Prevention in Ningxia Based on Night Light Remote Sensing Data

Poverty is a perennial challenge for humanity in the 21st century, especially in developing countries. China is the world's largest developing country and its effectiveness in reducing poverty can serve as a reference for other countries in implementing poverty reduction. Luminous remote sensing images can reflect changes in social and economic development and provide a new means of monitoring poor and backward areas. This paper selects Ningxia, a western region with deep poverty, as the study area, establishes the Sustainable Livelihoods Framework (SLA) multidimensional poverty indicator system, extracts the Total Night Light Index (TNL) and the Average Night Light Index (ANL) based on night light remote sensing images, and uses them to construct a night light poverty index model, analyze the spatial distribution characteristics of Ningxia's poor counties, and conduct an in-depth probe into the influencing factors of poverty in Ningxia. The analysis of the results shows that in the Ningxia region, the nighttime light poverty model (MPI TNL ) constructed using the total nighttime light index (TNL) is better, with an R2 of 0.792 for the MPI TNL model and an average relative error of 16.07%. Based on the poverty index fitted by MPI TNL , a grading of poverty was carried out at the county level scale, and it was found that the poverty level in Ningxia deepened from the north to the south, with the south-central part of Ningxia being the region most vulnerable to poverty, with the six main influencing factors being altitude, per capita disposable income of urban residents, per capita disposable income of rural residents, the proportion of employed workers, urbanization rate, and investment in fixed assets. Finally, a two-factor interaction detection between the main factors was carried out using a geographic probe, in which the interaction between the share of employed workers and fixed asset investment had the greatest explanatory power, and the combined effect of these two factors had the most significant impact on the divergence of poor counties in Ningxia, where the resource allocation of social capital and human capital influenced the economic development of Ningxia.