Abstract
As an objective social phenomenon, poverty has accompanied the vicissitudes of human society, which is a chronic dilemma hindering human civilization. Remote sensing data, such as nighttime lights imagery, provides abundant poverty-related information that can be related to poverty. However, it may be insufficient to rely merely on nighttime lights data, because poverty is a comprehensive problem, and poverty identification may be affected by topography, especially in some developing countries or regions where agriculture accounts for a large proportion. Therefore, some geographical features may be necessary for supplements. With the support of the random forest machine learning method, we extracted 23 spatial features base on remote sensing including nighttime lights data and geographical data, and carried out the poverty identification in Guizhou Province, China, since 2012. Compared with the identifications using support vector machines and the artificial neural network, random forest showed a better accuracy. The results supported that nighttime lights and geographical features are better than those only by nighttime lights features. From 2012 to 2019, the identified poor counties in Guizhou Province showed obvious dynamic spatiotemporal characteristics. The number of poor counties has decreased consistently and contiguous poverty-stricken areas have fragmented; the number of poor counties in the northeast and southwest regions decreased faster than other areas. The reduction in poverty probability exhibited a pattern of spreading from the central and northern regions to the periphery parts. The poverty reduction was relatively slow in areas with large slope and large topographic relief. When poor counties are adjacent to more non-poor counties, they can get rid of poverty easier. This study provides a method for feature selection and recognition of poor counties by remote sensing images and offers new insights into poverty identification and regional sustainable development for other developing countries and areas.
Highlights
Introduction nal affiliationsAs an objective social phenomenon, poverty has accompanied the vicissitudes of human society, which is a chronic dilemma hindering human civilization [1]
In order to make a comparison between the Random Forest (RF) and other machine learning models, we used two typical classification algorithms of Support Vector Machines (SVM) and Artificial Neural Network (ANN) to identify the poverty probability in 2012, and adopted four evaluation indicators, namely, accuracy, precision, recall, and F-value to evaluate the effects of these three models in poverty identification
Compared with ANN and SVM, the RF model reaches higher values in its accuracy, precision, recall and F-value, indicating the RF-based identification had a better performance in poverty identification
Summary
Introduction nal affiliationsAs an objective social phenomenon, poverty has accompanied the vicissitudes of human society, which is a chronic dilemma hindering human civilization [1]. The world’s largest developing country, has been undergoing rapid economic development [2]. China has taken a large number of comprehensive poverty-alleviation work and has achieved remarkable success in poverty reduction since the beginning of the economic reforms [3]. China was the first country in the world to successfully achieve the target of Millennium Development Goals of having extreme poverty in 2012, which was ahead of schedule [4]. There are still a large number of poor people, and, at the same time, new aspects of poverty have emerged in China. Poverty remains a serious issue for China’s modernization [5]. In Southwest China, the high altitude and mountainous terrain lead to the low efficiency of land use and the underdeveloped
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