Poverty-Returning Risk Monitoring and Analysis of the Registered Poor Households Based on BP Neural Network and Natural Breaks: A Case Study of Yunyang District, Hubei Province

Runqiao Zhang, Yawen He, Wenkai Cui, Ziwen Yang, Jingyu Ma, Haonan Xu and Duxian Feng
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Runqiao Zhang: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Yawen He: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Wenkai Cui: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Ziwen Yang: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Jingyu Ma: College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
Haonan Xu: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Duxian Feng: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China

Sustainability, 2022, vol. 14, issue 9, 1-18

Abstract: To address the problem of subjectivity in determining the poverty-returning risk among registered poor households, a method of monitoring and analyzing the poverty-returning risk among households based on BP neural network and natural breaks method was constructed. In the case of Yunyang District, Hubei Province, based on the data of the poverty alleviation and development system, we constructed a monitoring system for the poverty-returning risk for the registered poor households. The spatial distribution pattern of households under the poverty-returning risk was analyzed from two scales of district and township, respectively, by combining Geographic Information Science, and the influence degree of indicators on the poverty-returning risk using mean impact value (MIV). The results show that: (1) The spatial distribution of the poverty-returning risk among the registered poor households in the study area basically coincides with the local natural poverty-causing factors and the degree of social and economic development. (2) The Poverty-Returning Risk Index for each township represents a globally strong spatial dependence with a Moran’s I coefficient of 0.352. (3) The past poverty identification status of registered poor households is the main factor to reduce the poverty-returning risk, and the past policy should remain unchanged for a period of time. (4) Improving the quality of education within households and focusing on helping households with older average age can further reduce the poverty-returning risk.

Keywords: BP neural network; natural breaks; registered poor households; poverty-returning risk; spatial distribution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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