Exploring spatial patterns of human development through geovisualization: a case study of Java Island, Indonesia, 2023

Geovisualization serves as a vital tool for presenting multivariate data such as the Human Development Index (HDI). As a composite indicator, HDI measures the success of human development across three principal dimensions: health, education, and standard of living. Despite its multidimensional nature, most existing visualizations of HDI remain univariate, thereby limiting the ability to convey interrelationships among its constituent components. This study seeks to evaluate and compare the effectiveness of three multivariate geovisualization techniques – Chernoff Faces, Color Glyphs, and univariate choropleth maps – in representing HDI data at the regency/city level across Java Island for the year 2023. The dataset encompasses critical indicators including Life Expectancy (LE), Expected Years of Schooling (EYS), Mean Years of Schooling (MYS), and Purchasing Power Parity (PPP), sourced from publications by BPS-Statistics Indonesia. The methodological framework integrates usability testing employing both quantitative and qualitative approaches, including structured questionnaires and semi-structured interviews. The assessment focused on dimensions of effectiveness and user satisfaction, particularly evaluating ease of interpretation, accessibility, completeness of data representation, and visual appeal. Empirical findings indicate that the univariate choropleth map was rated as the most easily comprehensible and generally preferred by respondents, whereas the Chernoff Faces visualization was perceived as the most visually engaging.