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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146871" target="_blank" >RIV/00216305:26220/22:PU146871 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11036-022-02068-5" target="_blank" >https://link.springer.com/article/10.1007/s11036-022-02068-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11036-022-02068-5" target="_blank" >10.1007/s11036-022-02068-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

MOBILE NETWORKS & APPLICATIONS

ISSN

1383-469X

e-ISSN

1572-8153

Svazek periodika

2022

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

„“-„“

Kód UT WoS článku

000896491200001

EID výsledku v databázi Scopus

2-s2.0-85143618666