Extracting Patterns of Proximity in Regional Development Inequality Using Hierarchical Agglomerative Clustering

Abstract

Equitable regional development is still difficult to implement. Many interests in determining regional development priorities make it difficult to realize the main goals of national development. Political interests and the underutilization of regional statistical data are often other obstacles in carrying out regional development. This study aims to classify regional development data to be used to analyse existing development inequalities. Gross regional domestic income (GRDP) data is the main data in this study. The researcher used the hierarchical agglomerative clustering (HAC) technique. The results of the cluster can be used as a consideration for local governments to determine future regional development priorities. In addition, visualized cluster results in the form of a dendrogram can show the proximity of development inequality between regions.