Analyzing Global Development: A K-Means Clustering Approach to the 2023 Legatum Prosperity Index

This paper provides an in-depth analysis of global country development using the 2023 Legatum Prosperity Index dataset, which evaluates countries across dimensions such as Safety and Security, Governance, Personal Freedom, Economic Quality, Health, Education, and Social Capital. By employing Exploratory Data Analysis (EDA), the study uncovers trends and patterns, offering insights into the factors influencing prosperity. Using the KMeans clustering algorithm, countries are grouped into clusters with similar development profiles, distinguishing developed, developing, and underdeveloped nations. The analysis highlights the top 10 performing countries in key dimensions like Governance, Health, and Education, providing valuable insights into the drivers of national prosperity. The clustering results reveal how countries with strong Governance and Health systems tend to perform better in overall prosperity, while others lag due to deficiencies in these areas. It aims to guide policymakers by identifying key areas of improvement, offering a data-driven approach to enhancing national well-being. Future work could incorporate additional datasets, such as GDP and Human Development Index (HDI), and explore alternative clustering methods to gain a more comprehensive understanding of global development patterns.

Authors

Publication Details

Published
2024-11-06
DOI
https://doi.org/10.1109/icstsdg61998.2024.11026128
Primary Topic
Global Trade and Competitiveness
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analyzing Global Development: A K-Means Clustering Approach to the 2023 Legatum Prosperity Index

P. Ramya, S. Hemalatha, P Nithiyasree, C. Amritha et al.
Global Trade and Competitiveness
article

Analyzing Global Development: A K-Means Clustering Approach to the 2023 Legatum Prosperity Index

P. Ramya, S. Hemalatha, P Nithiyasree, C. Amritha, V. Subedha
article en

Abstract

This paper provides an in-depth analysis of global country development using the 2023 Legatum Prosperity Index dataset, which evaluates countries across dimensions such as Safety and Security, Governance, Personal Freedom, Economic Quality, Health, Education, and Social Capital. By employing Exploratory Data Analysis (EDA), the study uncovers trends and patterns, offering insights into the factors influencing prosperity. Using the KMeans clustering algorithm, countries are grouped into clusters with similar development profiles, distinguishing developed, developing, and underdeveloped nations. The analysis highlights the top 10 performing countries in key dimensions like Governance, Health, and Education, providing valuable insights into the drivers of national prosperity. The clustering results reveal how countries with strong Governance and Health systems tend to perform better in overall prosperity, while others lag due to deficiencies in these areas. It aims to guide policymakers by identifying key areas of improvement, offering a data-driven approach to enhancing national well-being. Future work could incorporate additional datasets, such as GDP and Human Development Index (HDI), and explore alternative clustering methods to gain a more comprehensive understanding of global development patterns.

Openalex Percentile: Top 48%
Global Trade and Competitiveness
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Analyzing Global Development: A K-Means Clustering Approach to the 2023 Legatum Prosperity Index — P. Ramya, S. Hemalatha, et al. · (2024) | TGRS Research Map | TGRS