Publication detail

Logistic Regression Analysis of Targeted Poverty Alleviation with Big Data in Mobile Network

ZHAO, W. HERENCSÁR, N.

Original Title

Logistic Regression Analysis of Targeted Poverty Alleviation with Big Data in Mobile Network

Type

journal article in Web of Science

Language

English

Original Abstract

In order to improve the identification accuracy and shorten the analysis time of poor households in poverty alleviation, this paper studies a logistic regression analysis algorithm of targeted poverty alleviation based on mobile big data. Based on the theories related to big poverty alleviation data, Apriori algorithm is used to mine the basic information of households collected through mobile network based on Maslow's hierarchy of needs theory. A multi-dimensional item data of poverty detection is obtained by analyzing the frequent itemsets of association rules in poor areas, and the poverty characteristics of poor areas from different dimensions are analyzed. Taking the big data platform of targeted poverty alleviation in Jiangxi Province, China, as an example, the economic assistance data is selected and sent into the k-means algorithm to cluster by taking the village as the unit. Then, combined with the correlation of poverty characteristics, the abnormal phenomena in poverty alleviation are found, and the effectiveness of the targeted assistance to poverty alleviation target areas is analyzed. Based on nonlinear logistic regression, the identification model of poor households is built, and the Spark frame is used to extract, transform and read the characteristics of samples respectively. Finally, the poor households are identified with the logistic regression algorithm. Experimental results show that the average recognition accuracy of poor households reaches 92%, and the mining time of poverty feature analysis is only 18 s, which improves the efficiency of data analysis than current algorithms.

Keywords

Big data mining; Targeted poverty alleviation; Mobile network; Maslow demand hierarchy; Apriori; k-means

Authors

ZHAO, W.; HERENCSÁR, N.

Released

9. 12. 2022

Publisher

SPRINGER

Location

NEW YORK

ISBN

1383-469X

Periodical

MOBILE NETWORKS & APPLICATIONS

Year of study

2022

Number

12

State

Kingdom of the Netherlands

Pages count

12

URL

https://link.springer.com/article/10.1007/s11036-022-02068-5

BibTex

@article{BUT180826,
  author="Wei {Zhao} and Norbert {Herencsár}",
  title="Logistic Regression Analysis of Targeted Poverty Alleviation with Big Data in Mobile Network",
  journal="MOBILE NETWORKS & APPLICATIONS",
  year="2022",
  volume="2022",
  number="12",
  pages="12",
  doi="10.1007/s11036-022-02068-5",
  issn="1383-469X",
  url="https://link.springer.com/article/10.1007/s11036-022-02068-5"
}