Application of spatial ‘K’luster analysis by tree edge removal (SKATER) on infrastructure inequality mapping

Clustering is an analytical method aiming to group a set of objects into two or more groups based on the similarity of characteristics in these groups. The stage of clustering begins with reducing large-dimensional data into small-dimensional data because there is multicollinearity. One of the regionalization methods for grouping based on location with spatial autocorrelation and the spatial pattern is K’luster by Tree Edge Removal (SKATER) method. This method combines the contiguity matrix with the distance matrix as a cost calculation to select the minimum connectivity according to the principle of the prim’s algorithm. The best cluster is selected by calculating the partition quality value using the intracluster sum square of standard deviation. This study aimed to map infrastructure inequality in Bantul Regency in 2019. The results show that there is a spatial pattern and get the best three partition groups for grouping with a partition quality value of Q(Π) of 10.0333.