Poverty and inequality remain outstanding challenges in many global regions. Understanding the underlying social and economic conditions is important in formulating poverty eradication strategies. Using Visible Infrared Imaging Radiometer Suite (VIIRS) Night-Time Light (NTL) images and multidimensional socioeconomic data between 2012 and 2018, this study measured regional poverty and inequality in the Xiamen-Zhangzhou-Quanzhou city cluster in the People’s Republic of China. Principal Component Analysis (PCA) and the Theil index decomposition method were used to establish an Integrated Poverty Index (IPI) and a regional inequality index, respectively. The results indicated that: (1) The poverty index is affected by the geographical location, policies, and resources of a district/county. A significant logarithmic correlation model between VIIRS Average Light Index (ALI) and IPI was established. (2) The Theil index derived from Gross Domestic Product (GDP) indicators showed that overall inequality and between-prefecture inequality declined, while within-prefecture inequality remained unchanged. In terms of the contributions to regional inequality, the contribution of within-prefecture inequality is the largest. The results indicated that Suomi National Polar Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) night-time data can help to perform district/county-level poverty assessments at small and medium spatial scales, although the evaluation effect on regional inequality is slightly lower.
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