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Application of Power Big Data in Targeted Poverty Alleviation—Taking Poverty Counties in Jiangxi Province as an Example

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Data Science (ICDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1179))

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Abstract

Targeted poverty alleviation is an important measure to promote China’s all-round development, but traditional economic surveys and statistics are limited by multiple factors, making it difficult to accurately identify poor targets in a timely manner. The development of power big data provides the possibility to use energy consumption data to locate and identify poor areas. Therefore, this article takes Jiangxi Province as an example to analyze 23 regions that have been classified as poverty-stricken counties (8 counties have been separated from the list of impoverished counties). First, panel data regression is performed to prove that electricity sales can be used to analyze and predict regional economic development. Then, using decision tree ID3 algorithm and four neural network algorithms to classify and forecast poor and non-poor counties, it is found that ID3 algorithm has good fitting and prediction accuracy. Therefore, power big data can be applied to the work of targeted poverty alleviation, and has a good prospect.

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Notes

  1. 1.

    A method for defining the weight of the poor, the per capita income of farmers, and the per capita GDP.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China No. 71501175, the University of Chinese Academy of Sciences, and the Open Project of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences.

Author information

Authors and Affiliations

  1. China University of Political Science and Law, Beijing, 102249, China

    Jing Mengtong & Huang Zili

  2. Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, China

    Liu Kefan

  3. University of Chinese Academy of Sciences, Beijing, 100190, China

    Guo Kun

Authors
  1. Jing Mengtong
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  2. Liu Kefan
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  3. Huang Zili
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  4. Guo Kun
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Corresponding authors

Correspondence to Jing Mengtong or Guo Kun .

Editor information

Editors and Affiliations

  1. Swinburne University of Technology, Melbourne, VIC, Australia

    Jing He

  2. University of Illinois at Chicago, Chicago, USA

    Philip S. Yu

  3. College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA

    Yong Shi

  4. Research Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou, China

    Xingsen Li

  5. Ningbo University, Ningbo, China

    Zhijun Xie

  6. Deakin University, Burwood, VIC, Australia

    Guangyan Huang

  7. Department of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China

    Jie Cao

  8. Nanjing University of Posts and Telecommunications, Nanjing, China

    Fu Xiao

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Cite this paper

Mengtong, J., Kefan, L., Zili, H., Kun, G. (2020). Application of Power Big Data in Targeted Poverty Alleviation—Taking Poverty Counties in Jiangxi Province as an Example. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_10

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  • DOI: https://doi.org/10.1007/978-981-15-2810-1_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2809-5

  • Online ISBN: 978-981-15-2810-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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