Monitoring the spatiotemporal dynamics of poor counties in China: Implications for global sustainable development goals. (1st August 2019) - Li, Guie; Chang, Liyun; Liu, Xiaojian; Su, Shiliang; Cai, Zhongliang; Huang, Xinran; Li, Bozhao
Abstract: Poverty remains one of the long-term chronic dilemmas facing the sustainable development of human society during the 21st century. The spatiotemporal dynamics of poor regions, particularly in developing countries, is crucial for realizing fundamental sustainable development goals (SDGs). For decades, many scholars have sought to accurately measure, identify and alleviate poverty at different geographical scales. However, reliable data about the estimation of poverty remain scarce for developing countries, hindering efforts to accurately identify poverty. This paper utilizes the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) sensor nighttime light imagery to identify poor counties in China from 1992 to 2013. Using 16 statistical and spatial features extracted from this nighttime light imagery and using 96 poor counties and 96 nonpoor counties from 2010 as the classification sample, we describe the spatiotemporal dynamics of poor counties based on a random forests approach. Our study finds that the number of poor counties is decreasing in a fluctuating pattern and that contiguous poverty-stricken areas are becoming fragmented. The reduction in poor counties exhibits a manner of moving horizontally from the eastern regions to the central and western parts of China, while the number of poor counties in the central and western regions has decreased around the central cities or areas. The Aihui-Tengchong Line is not the dividing line in the distribution of poor counties in China, which means that China's poor can also be found in areas with relatively high population density. Together, the findings reveal that the key to reducing regional poverty is the development of regional economies and the implementation of national macro policies. This paper provides references for formulating antipoverty strategies for each county and offers new insights into poverty estimation and regional sustainable development for other developing countries. Graphical abstract: Image 1 Highlights: A new research framework is proposed to identify poverty. Imagery and random forests are used to classify poor counties. Spatiotemporal dynamics of poor counties are monitored in China. Poverty dynamics generate implication for poverty policy and sustainable development.