Tang, L., Cheng, H., & Qu, G. (2013). Estimating Provincial Economic Development Level of China Using DMSP/OLS Nighttime Light Satellite Imagery. Amr, 807-809, 1903–1908.
Abstract: How to estimate regional economic development level is important for solving regional inequality problems. Most of previous studies on regional economic development are based on the statistics collected typically in administrative units. This paper has analyzed the defects of traditional studies, and attempted to research regional economic development problems with 10-year DMSP/OLS nighttime light satellite imagery as a new data source. For exploring the relationship between DMSP/OLS nighttime light data and GDP, different types of curve fitting regression models have been tried, the Cubic model has shown the best performance with a coefficient of determination (R2) equal to 0.803. Based on this positive correlation, we have estimated provincial economic development level of China using DMSP/OLS nighttime light data. The research results have indicated that the DMSP/OLS nighttime light data can well reveal provincial economic development levels.
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Wang, W., Cheng, H., & Zhang, L. (2012). Poverty assessment using DMSP/OLS night-time light satellite imagery at a provincial scale in China. Advances in Space Research, 49(8), 1253–1264.
Abstract: All countries around the world and many international bodies, including the United Nations Development Program (UNDP), United Nations Food and Agricultural Organization (FAO), the International Fund for Agricultural Development (IFAD) and the International Labor Organization (ILO), have to eliminate rural poverty. Estimation of regional poverty level is a key issue for making strategies to eradicate poverty. Most of previous studies on regional poverty evaluations are based on statistics collected typically in administrative units. This paper has discussed the deficiencies of traditional studies, and attempted to research regional poverty evaluation issues using 3-year DMSP/OLS night-time light satellite imagery. In this study, we adopted 17 socio-economic indexes to establish an integrated poverty index (IPI) using principal component analysis (PCA), which was proven to provide a good descriptor of poverty levels in 31 regions at a provincial scale in China. We also explored the relationship between DMSP/OLS night-time average light index and the poverty index using regression analysis in SPSS and a good positive linear correlation was modelled, with R2 equal to 0.854. We then looked at provincial poverty problems in China based on this correlation. The research results indicated that the DMSP/OLS night-time light data can assist analysing provincial poverty evaluation issues.
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