Geographic Characterisation of Children's Health and Wellbeing through Vulnerability Indices Using Principal Component Analysis (PCA) and K-Means Clustering

2025/08/09 by Harriette Phillips, Phillips, Harriette, Aiden Price +3
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Advanced Clustering Algorithms Research #FOS: Mathematics #General Mathematics (math.GM) #Reliability and Agreement in Measurement #Spatial and Panel Data Analysis

paper · pdf · doi:10.48550/arxiv.2509.14236

openalex publication_date 2025/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Using Australian children as a case study, this paper aims to develop vulnerability indices to characterise the health and well-being of children aged 0-5 years old. The indices are used to identify differences in children's health and well-being across geographic regions and identify clusters of regions with similar characteristics. The approach is underpinned by two well-known statistical methods, namely Principal Component Analysis and K-Means Clustering. The identification of these regions with similar vulnerability characteristics can then be used to derive new insights into drivers of children's health and well-being and support improved decision-making for services in Australia.

Related

• Assessing the Spatial Structure of the Association between Attendance at Preschool and Childrens Developmental Vulnerabilities in Queensland Australia
• Toward Improving Predictive Risk Modelling for New Zealand's Child Welfare System Using Clustering Methods
• Being a victim of bullying reduces child subjective well-being substantively: an international comparison
• The Ecology of Risk and the Geography of Intervention: From Research to Practice for the Health and Well-being of Urban Children
• Significance of condition attributes in child well-being analysis