Moran I Autocorrelation Study For Level Spatial Pattern Analysis

Abstract

Unemployment is a serious challenge faced by developing countries such as Indonesia. These challenges involve complex factors interacting with each other and can have a negative impact on social and economic stability. This study focused on East Java Province as a case in point, with the aim of analyzing the geographical distribution of open unemployment (TPT) and the relationship between regions in that context. Using a spatial analysis approach, specifically the Moran's I autocorrelation method, this study seeks to uncover spatial patterns and spatial interactions related to TPT levels. Quantitative data were used to identify TPT distribution patterns in this region. The results of spatial autocorrelation analysis indicate that the distribution of TPT in East Java Province tends to be random. Although there are spatial patterns that can be identified based on the Moran index, the z-score results show that they are not significantly different from random patterns. From the results of the Moran's I quadrant, it can be seen that there are several areas with high TPT rates around other regions that also have high TPT rates. Thus, this research contributes to formulating policies and actions aimed at reducing unemployment, improving people's welfare, and preventing potential social insecurity and poverty.