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Article

Identification of Poverty Areas by Remote Sensing and Machine Learning: A Case Study in Guizhou, Southwest China

by 1,2,*, 1,2 and 1,2
1
Center for China Western Modernization, Guizhou University of Finance and Economics, University City, Huaxi District, Guiyang 550025, China
2
College of Big Data Application and Economic, Guizhou University of Finance and Economics, University City, Huaxi District, Guiyang 550025, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010011
Received: 11 November 2020 / Revised: 21 December 2020 / Accepted: 27 December 2020 / Published: 30 December 2020
(This article belongs to the Special Issue Geo-Information Technology and Its Applications)
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. View Full-Text
Keywords: poverty probability; random forest; nighttime lights; spatiotemporal characteristics poverty probability; random forest; nighttime lights; spatiotemporal characteristics
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MDPI and ACS Style

Yin, J.; Qiu, Y.; Zhang, B. Identification of Poverty Areas by Remote Sensing and Machine Learning: A Case Study in Guizhou, Southwest China. ISPRS Int. J. Geo-Inf. 2021, 10, 11. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010011

AMA Style

Yin J, Qiu Y, Zhang B. Identification of Poverty Areas by Remote Sensing and Machine Learning: A Case Study in Guizhou, Southwest China. ISPRS International Journal of Geo-Information. 2021; 10(1):11. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010011

Chicago/Turabian Style

Yin, Jian, Yuanhong Qiu, and Bin Zhang. 2021. "Identification of Poverty Areas by Remote Sensing and Machine Learning: A Case Study in Guizhou, Southwest China" ISPRS International Journal of Geo-Information 10, no. 1: 11. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010011

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