Poverty classification using Analytic Hierarchy Process and k-means clustering

Sarwosri,; Sunaryono, Dwi; Akbar, Rizky Januar; Setiyawan, Risky Dwi

doi:10.1109/icts.2016.7910310

Cited by 6 publications

(8 citation statements)

References 5 publications

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“…Liu et al [43] divided poverty into eight types: human capital poverty, financial capital poverty, infrastructure poverty, human infrastructure poverty, financial infrastructure poverty, living conditions poverty, livelihood poverty and development conditions poverty. Sarwosri et al [44] divided poverty into near poor, poor, and very poor.…”

Section: Poverty Typesmentioning

confidence: 99%

“…Referring to the research of Ravallion and Chen [19], Sarwosri et al [44], Zhou and Ye [23] and Zheng [22], according to the annual per capita income of farmers, the poverty types of farmers are divided into the following three categories: absolutely poor type, relatively poor type and non-poor type. Among them, the absolutely poor type refers to the farmers whose annual per capita net income is lower than China's poverty standard in 2018; the relatively poor type refers to the farmers whose annual per capita net income is within the floating range of the relative poverty line; the non-poor type refers to the farmers whose annual per capita net income is higher than the floating range of relative poverty line.…”

Section: Poverty Typementioning

confidence: 99%

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The Impact of Livelihood Risk on Farmers of Different Poverty Types: Based on the Study of Typical Areas in Sichuan Province

Zeng

Deng

et al. 2021

Agriculture

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After the completion of China’s poverty alleviation task in 2020, the poverty situation will undergo major changes, and the focus of poverty alleviation work will shift to solving relative poverty. This can provide useful inspiration for the government to formulate and implement relevant policies that explore the differences in livelihood risk impacts of different types of farmers in areas where disasters and poverty are intertwined. This study used survey data of 327 households in four districts and counties in the hardest-hit areas of the Wenchuan Earthquake and Lushan Earthquake in China’s Sichuan Province in 2018. This study measured farmers’ livelihood risks from four aspects: health risks, environmental risks, financial risks, and social risks, and measured farmers’ poverty types from three aspects: absolutely poor farmers, relatively poor farmers, and non-poor farmers. It systematically analyzed the four types of livelihood risks faced by farmers and the three types of poverty they were in, and constructed a multinomial logistic regression to explore the correlation between livelihood risks and poverty types. This study is the first to compare the relatively poor type with other poverty types. The second innovation is that it uses the entropy method and multinomial logistic regression. The results showed that: (1) Among the four livelihood risks faced by farmers, the biggest was the environmental risk, the next was financial risk, the third was health risk, and the last was social risk. (2) Among the three poverty types, farmers in absolute poverty were the most populated, with non-poor farmers next, and relatively poor farmers at the bottom. (3) Farmers of different poverty types were affected by livelihood risks to different degrees. Specifically, when compared with the impacts on farmers in absolute poverty, the impacts social risks could bring to farmers in relative poverty were more severe while the impacts of health risks, environmental risks and financial were not that strong. Impacted by social risks, relatively poor farmers are more seriously impacted by public affairs and social security status. When compared with the impacts on non-poor farmers, the impacts of all these four risks on relatively poor farmers were not notable.

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Section: Poverty Typesmentioning

confidence: 99%

Section: Poverty Typementioning

confidence: 99%

The Impact of Livelihood Risk on Farmers of Different Poverty Types: Based on the Study of Typical Areas in Sichuan Province

Zeng

Deng

et al. 2021

Agriculture

View full text Add to dashboard Cite

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“…Saat ini jumlahnya mencapai 4,77 % [1], [4], [5]. Pemerintah terus berupaya untuk menekan angka kemiskinan melalui berbagai program seperti Program Keluarga Harapan (PKH), Beras Sejahtera, bantuan hunian sederhana (Rutihalu), Santunan Warga Tidak Mampu (SWTM) [2], [6], [7]. Pengentasan kemiskinan tentunya diharapkan mampu menciptakan kesejahteraan masyarakat, yang mana peningkatan kesejahteraan masyarakat mencerminkan upaya pembangunan berjalan dengan baik.…”

Section: Pendahuluanunclassified

“…Menghitung nilai ( , ') Menentukan fungsi preferensi yang kuat ( , '). Fungsi preferensi yang kuat berdasarkan perbandingan nilai-nilai perbedaan (dmj) dengan rentang nilai seperti yang didefinisikan oleh evaluasi dari seluruh rangkaian alternatif untuk kriteria pada persamaan (7).…”

Section: Metode the Extended Promethee IIunclassified

Penentuan Prioritas Kesejahteraan Keluarga Menggunakan Metode the Extended Promethee II

2020

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“…They found out that the ratio of sex, income and education were the crucial contributing factors in the non-poor group while dependence rate and family size were the crucial contributing factors in the poor group. Apart from that, the Analytic Hierarchy Process (AHP) was applied for poverty classification, while K-Means clustering was used to determine the range values between clusters [24]. Likewise, Coromaldi and Drago [25] employed the K-Means algorithm to explain poverty in Italy through an in-depth study of the income-deprivation score relationship.…”

Section: Introductionmentioning

confidence: 99%

A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group

et al. 2021

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The Multidimensional Poverty Index (MPI) is an income-based poverty index which measures multiple deprivations alongside other relevant factors to determine and classify poverty. The implementation of a reliable MPI is one of the significant efforts by the Malaysian government to improve measures in alleviating poverty, in line with the recent policy for Bottom 40 Percent (B40) group. However, using this measurement, only 0.86% of Malaysians are regarded as multidimensionally poor, and this measurement was claimed to be irrelevant for Malaysia as a country that has rapid economic development. Therefore, this study proposes a B40 clustering-based K-Means with cosine similarity architecture to identify the right indicators and dimensions that will provide data driven MPI measurement. In order to evaluate the approach, this study conducted extensive experiments on the Malaysian Census dataset. A series of data preprocessing steps were implemented, including data integration, attribute generation, data filtering, data cleaning, data transformation and attribute selection. The clustering model produced eight clusters of B40 group. The study included a comprehensive clustering analysis to meaningfully understand each of the clusters. The analysis discovered seven indicators of multidimensional poverty from three dimensions encompassing education, living standard and employment. Out of the seven indicators, this study proposed six indicators to be added to the current MPI to establish a more meaningful scenario of the current poverty trend in Malaysia. The outcomes from this study may help the government in properly identifying the B40 group who suffers from financial burden, which could have been currently misclassified.

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Poverty classification using Analytic Hierarchy Process and k-means clustering

Cited by 6 publications

References 5 publications

The Impact of Livelihood Risk on Farmers of Different Poverty Types: Based on the Study of Typical Areas in Sichuan Province

The Impact of Livelihood Risk on Farmers of Different Poverty Types: Based on the Study of Typical Areas in Sichuan Province

Penentuan Prioritas Kesejahteraan Keluarga Menggunakan Metode the Extended Promethee II

A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group

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