Abstract
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.
Highlights
From the Millennium Development Goals (MDG) of the United Nations in 2000 to the Sustainable Development Goals (SDG) in 2015, eliminating poverty and inequality has been one of the top global priorities
The above quadratic function relationship indicates that the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) average night-time light data can be an effective representation of the economic activity and can better estimate the regional poverty level of the Xiamen-Zhangzhou-Quanzhou city cluster
The results show that the Theil index calculated by NPP/VIIRS Night-Time Light (NTL) and Gross Domestic Product (GDP) shows a downward trend from 2012 to 2018, indicating that the overall regional inequality gap in the urban agglomeration was narrowing
Summary
From the Millennium Development Goals (MDG) of the United Nations in 2000 to the Sustainable Development Goals (SDG) in 2015, eliminating poverty and inequality has been one of the top global priorities. The issue of how to measure regional poverty and inequality in a timely, accurate and effective manner is a problem that needs to be overcome In this context, the emergence of the Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) remote sensing Night-Time Light (NTL) data provides a new perspective for exploring human economic activities. Due to the above advantages of NTL data, some scholars have used night-time light data to study regional poverty and inequality [5,6,7,9,10,26,27]. Christian et al used DMSP/OLS night-time lighting data to predict local per capita income in 180 countries from 1992 to 2012 Based on these projections, income inequality in different regions was calculated [6]. The factors affecting regional poverty and inequality in city clusters are analyzed
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