Cluster Analysis Using K-Means Method to Classify Indonesia Regency/City based on Human Development Index Indicator

Successful development of the quality of human life in a region is determined by the Human Development Index (HDI). Human development performance based on the HDI can be measured: long and healthy life, knowledge, and a decent standard of living. The HDI is usually grouped into several categories to facilitate the classification of the HDI level of each region. This study aimed to determine the ability of the bootstrap aggregating (bagging) method to classify the HDI by district/city. Bagging is a stochastic machine learning approach that can eliminate the variance of the classifier by producing a bootstrap ensemble to obtain better accuracy results. The dependent variable in this study was the HDI by district/city in 2020. In contrast, life expectancy at birth, expected years of schooling, mean years of schooling, and real expenditure per capita are adjusted as independent variables. Bagging was applied to the high and low categories of HDI data. The bagging method demonstrated good classification performance due to only eight classification errors, namely the HDI data which should be in the high category but classified into the low category by the bagging method. Based on the results of calculations with 25 replications, it can be concluded that the bagging method has a very good performance, with an accuracy value of 92.3%, the sensitivity of 100%, and specificity of 83.33%. The bagging method is considered very good for the classifying the HDI by district/city in Indonesia in 2020 because it has a balanced accuracy of 91.67%.

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Mathematical Analysis and Modeling of the Factors That Determine the Quality of Life in the City Councils of Chile

Ríos-Vásquez

Fuente‐Mella

2023

Mathematics

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The quality of life index is an indicator published yearly since 2010 by the Institute on Urban and Territorial Studies and the Chilean Chamber of Construction, involving 99 municipalities and communes from the national territory. This research provides an approach to understanding how various dimensions and variables interact with quality of life in Chilean communes considering multiple factors and perspectives through information from public sources and social indicators. For the research, variables were analyzed considering demographic, sociodemographic, economics and urban indicators, where the model developed allows for an understanding of how the variables are related. In addition, it was discovered that education, own incomes, municipal spending and green areas directly relate to quality of life, while overcrowding and municipal funds negatively affect rates of communal welfare. Moreover, the variables chosen as explanatory variables allow for the development of an efficiency model. For this purpose, Cobb–Douglas and trans-logarithmic forms were tested, and it was found that Cobb–Douglas fits better to the data set and structures of the variables. The results of the efficiency model show that education, municipal funds and own incomes significantly affect efficiency, with a mean value of approximately 47%, minimum values close to 30% and maximum values of approximately 60%. Finally, a cluster analysis was developed through k-means, k-medoids and hierarchical clustering algorithms, where, in all cases, the results were similar, suggesting four groups with differences and variations in analyzed variables, especially in overcrowding, education, quality of life and wellness.

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Cluster Analysis Using K-Means Method to Classify Indonesia Regency/City based on Human Development Index Indicator

Cited by 3 publications

References 3 publications

Global Prosperity or Local Monopoly? Understanding the Geography of App Popularity

Global Prosperity or Local Monopoly? Understanding the Geography of App Popularity

Classification of the Human Development Index in Indonesia Using the Bootstrap Aggregating Method

Mathematical Analysis and Modeling of the Factors That Determine the Quality of Life in the City Councils of Chile

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