Poverty Measure Based on Hesitant Fuzzy Decision Algorithm under Social Network Media

Abstract

This study aims to solve the problem that the traditional method of measuring the poverty level in rural and urban areas of China from a purely monetary perspective can’t comprehensively analyze and reflect the poverty. In this study, a multidimensional poverty measurement model with non-monetary indicators is proposed, the data of families and their members provided by the China Health and Nutrition Survey (CHNS) of a certain year’s health and nutrition survey in China are used for analysis, and a fuzzy set method is adopted to analyze the poverty situation in various regions of China. First, the fuzzy function set method is used to calculate the one-dimensional poverty index. On the basis of income, the multi-dimensional poverty fuzzy index is calculated from five dimensions, including education, health, assets, and living standard. The calculation results of the single-dimensional poverty and the multi-dimensional poverty are compared to further analyze the reasons of the family poverty of rural residents. Second, the poverty rate of each dimension in each region is calculated by referring to the appropriate measurement indexes of each dimension of the message passing interface (MPI) team. The results show that the concept of measuring poverty by the fuzzy set method is more sensitive to the overall distribution of population in the poverty dimension than the poverty line method. Compared with the poverty line method, the fuzzy set method can better consider the overall distribution of population in poverty dimension. Accordingly, China should strengthen the infrastructure construction in rural areas, increase the investment in education in rural areas, and improve the overall quality of the poor population.

"Domains of Deprivation Framework" for Mapping Slums, Informal Settlements, and Other Deprived Areas in LMICs to Improve Urban Planning and Policy: A Scoping Review

“…Few frameworks have been developed for application in a Global or Global South context. Fukuda et al, 2007;Baud et al, 2008;Ling, 2009;Baud et al, 2009;Tipayamongkholgul et al, 2013;Chen & Wang, 2015;Mahadevan & Hoang, 2016;Webster et al, 2016;Chowdhury & Mukhopadhaya, 2016;Akter & Rahman, 2017;Ge et al, 2017;Manap et al, 2017;Wan & Su, 2017;Wu & Qi, 2017;Zakaria et al, 2017;Mitra & Nagar, 2018;Saif-Ur-Rahman et al, 2018;Bag & Seth, 2018;Yuan et al, 2018;Ajami et al, 2019;Pairan et al, 2019;Chen et al, 2019;Gao & Sun, 2020;Ensor et al, 2020 Europe & Central Asia 28.7 Jarman, 1983Jarman, , 2001Kearns, Gibb & Mackay, 2000;Bradshaw & Finch, 2003;Harris & Longley, 2004;Jordan et al, 2004;Scottish Executive, 2004Niggebrugge et al, 2005;Noble et al, 2006;Eroǧlu, 2007;Havard et al, 2008;European Commission, 2010;Marí-Dell'Olmo et al, 2011;Martínez & Lechuga, 2012;Payne & Abel, 2012;Bayram et al, 2012;…”

“…To reduce the high dimensionality (large number) of indicators that reflect deprivation and to deal with high correlation between indicators, several studies used dimension reduction strategies, such as factor analysis or principal component analysis as well as data-driven methods that allow the generation of clusters (Marí-Dell'Olmo et al, 2011; Krishnan, 2015; Roy et al, 2020) ( Figure 6). In recent years, advancements in methods such as artificial intelligence (AI) have enabled additional analyses of multiple deprivation (Ajami et al, 2019), as well as the development of deprivation measures in relation to fuzziness of concepts (Gao & Sun, 2020). Developments such as these are designed to address limitations of simple summative indices that obfuscate the complexity of deprivation.…”

“Domains of Deprivation Framework” for Mapping Slums, Informal Settlements, and Other Deprived Areas in LMICs to Improve Urban Planning and Policy: A Scoping Review

“…Few frameworks have been developed for application in a Global or Global South context. (Fukuda et al, 2007;Baud et al, 2008;Ling, 2009;Baud et al, 2009 Bag & Seth, 2018; Yuan et al, 2018;Ajami et al, 2019;Pairan et al, 2019;Chen et al, 2019;Gao & Sun, 2020;Ensor et al, 2020) Europe & Central Asia 28. 4 Jarman, 19834 Jarman, , 2001Kearns, Gibb & Mackay, 2000;Bradshaw & Finch, 2003;Harris & Longley, 2004;Jordan et al, 2004;Scottish Executive, 2004Niggebrugge et al, 2005;Noble et al, 2006;Eskandrani, 2007;Eroǧlu, 2007;Havard et al, 2008;European Commision, 2010;Marí-Dell'Olmo et al, 2011;Martínez & Lechuga, 2012;Payne & Abel, 2012;Bayram et al, 2012;Coromaldi & Zoli, 2012;Bocquier et al, 2013;Alguacil & Camacho, 2014;DCLG, 2015;Guillaume et al, 2016;Jacobsen, 2016;Arribas-Bel, Patino & Duque, 2017;Swiader et al, 2017;Cornado et al, 2017;Ministerio de Fomento, 2018;Venerandi et al, 2018;Abarca-Alvarez et al, 2019;Mclennan et al, 2019;Page et al, 2019;…”

“…To reduce the high dimensionality (large number) of indicators that reflect deprivation and to deal with high correlation between indicators, several studies used dimension reduction strategies, such as factor analysis or principal component analysis as well as data-driven methods that allow the generation of clusters (Marí-Dell'Olmo et al, 2011;Krishnan, 2015;Roy et al, 2020) (Figure 6). In recent years, advancements in methods such as artificial intelligence (AI) have enabled additional analyses of multiple deprivation (Ajami et al, 2019), as well as the development of deprivation measures in relation to fuzziness of concepts (Gao & Sun, 2020). Developments such as these are designed to address limitations of simple summative indices that obfuscate the complexity of deprivation.…”

Research on the Evaluation Modeling Method of User Experience Quality under Uncertain Noise Environment

“…Since user experience evaluation is influenced by the environmental, products, users, and other comprehensive factors, such as the environmental dimension, the natural environment, and the human and social environment, the user level includes the user's expectation, experience, the state of the user's body and mind, as well as the background of the user's experience [24,25]. e evaluator is often affected by individual and environmental subjective and objective factors, and the evaluation is uncertain to a great extent; this difference in individuals and in the environment gives varying evaluation results [26,27].…”