Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Zhang G, Zhang N (2020) The effect of China’s pilot carbon emissions trading schemes on poverty alleviation: A quasi-natural experiment approach. J Environ Manage 271:110973

    Article  Google Scholar 

  2. Li J, Wang Z, Cheng X, Shuai J, Shuai C, Liu J (2020) Has solar PV achieved the national poverty alleviation goals? Empirical evidence from the performances of 52 villages in rural China. Energy 201:117631

    Article  Google Scholar 

  3. Niu T, Chen Y, Yuan Y (2020) Measuring urban poverty using multi-source data and a random forest algorithm: A case study in Guangzhou. Sustain Cities Soc 54:102014

    Article  Google Scholar 

  4. Liu W, Li J, Zhao R (2020) Rural public expenditure and poverty alleviation in China: a spatial econometric analysis. J Agric Sci 12(6):46–53

    Google Scholar 

  5. Chattopadhyay AK, Kumar TK, Rice I (2020) A social engineering model for poverty alleviation. Nat Commun 11(1):6345

    Article  Google Scholar 

  6. Lin YK, Chen SG (2020) Reliability evaluation in terms of flow data mining for multistate networks. Ann Oper Res 3:230–238

    Google Scholar 

  7. Shi K, Chang Z, Chen Z et al (2020) Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China. J Clean Prod 255:120245

    Article  Google Scholar 

  8. Wei L, Yang Y (2019) Development trend of sharing economy in big data era based on duplication dynamic evolution game theory. Clust Comput 22(5):13011–13019

    Article  Google Scholar 

  9. Liu Z, Gao P, Li W (2022) Research on big data-driven rural revitalization sharing cogovernance mechanism based on cloud computing technology. Wirel Commun Mob Comput 6:1–9

    Google Scholar 

  10. Gao S, Zhou C (2020) Differential privacy data publishing in the big data platform of precise poverty alleviation. Soft Comput 24(11):8139–8147

    Article  Google Scholar 

  11. Wu S, Liu J, Liu L (2019) Modeling method of internet public information data mining based on probabilistic topic model. J Supercomput 75(4):5882–5897

    Article  Google Scholar 

  12. Shuai L, Xiyu X, Yang Z et al (2022) A reliable sample selection strategy for weakly-supervised visual tracking. IEEE Trans Rel, online first, https://doi.org/10.1109/TR.2022.3162346

  13. Zhou Y, Zhang J, Zeng Y (2021) Borrowing or crowdfunding: a comparison of poverty alleviation participation modes considering altruistic preferences. Int J Prod Res 59(21):6564–6578

    Article  Google Scholar 

  14. Liu H, Liu Y, Zhang R et al (2021) A clustering algorithm via density perception and hierarchical aggregation based on urban multimodal big data for identifying and analyzing categories of poverty-stricken households in China. Sci Program 1:1–13

    Google Scholar 

  15. Meilă M (2019) Good (K-means) clusterings are unique (up to small perturbations). J Multivar Anal 173:1–17

    Article  MathSciNet  MATH  Google Scholar 

  16. Li L, Xiaoming M (2022) Study on the exploration of poverty index’s association rules based on CBCM-Apriori algorithm. Ann Oper Res 2:1–27

    Google Scholar 

  17. Fernandez-Basso C, Ruiz MD, Martin-Bautista MJ (2020) A fuzzy mining approach for energy efficiency in a Big Data framework. IEEE Trans Fuzzy Syst 28(11):2747–2758

    Article  Google Scholar 

  18. Liu S, Wang S, Liu X et al (2021) Fuzzy detection aided real-time and robust visual tracking under complex environments. IEEE Trans Fuzzy Syst 29(1):90–102

    Article  Google Scholar 

  19. Zhang Y (2020) On mining of frequent item sets of big data based on K-means clustering. Computer Simulation 37(8):457–461

    Google Scholar 

  20. Shuai L, Chunli G, Fadi A et al (2020) Reliability of response region: a novel mechanism in visual tracking by edge computing for IIoT environments. Mech Syst Signal Process 138:106537

    Article  Google Scholar 

  21. Abd Alwahab OA, Abd Alrazak MS (2020) Using nonlinear dimensionality reduction techniques in big data analysis. Periodicals Eng Nat Sci 8(1):142–155

    Google Scholar 

  22. Peng C, Zhou X, Liu S (2022) An introduction to artificial intelligence and machine learning for online education. Mobile Netw Appl 27(3): 1147-1150

  23. S Liu, P Gao, Y Li et al (2023) Multi-modal fusion network with complementarity and importance for emotion recognition. Information Sciences 619:679–694

    Article  Google Scholar 

Download references

Acknowledgements

The paper is supported by Humanities and Social Sciences Research Project of Jiangxi Provincial Universities with No.GL20147; Science and Technology Project of Department of Education of Jiangxi Province with No.GJJ209301; Project of Key Laboratory of 5G Wireless Network Optimization of Nanchang City with No.2020-NCZDSY-015; Project of Key Laboratory of Mobile Communication of Nanchang City with No.2018-NCZDSY-008.

Author information

Authors and Affiliations

  1. Nanchang Jiaotong Institute, Nanchang, 330100, China

    Wei Zhao

  2. Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 616 00, Brno, Czechia

    Norbert Herencsar

Authors
  1. Wei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Norbert Herencsar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Norbert Herencsar.

Ethics declarations

Conflict of Interest

The authors have no relevant financial or non-financial interests to disclose. Wei Zhao provided the algorithm and experimental results, wrote the manuscript, Norbert Herencsar revised the paper, supervised and analyzed the experiment. We also declare that data availability and ethics approval is not applicable in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, W., Herencsar, N. Logistic Regression Analysis of Targeted Poverty Alleviation with Big Data in Mobile Network. Mobile Netw Appl 27, 2553–2564 (2022). https://doi.org/10.1007/s11036-022-02068-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-022-02068-5

Keywords

Search

Navigation