BYM CARBayes Model on data of unemployment in East Java and thus mapping using LISA analysis

Abstract

East Java is the biggest province in Indonesia with 38 regions that contribute to much unemployment in Indonesia. This article aims to model unemployment in each region by taking any factor that significantly influences the number of unemployment using the BYM CARBayes model. The previous research about spatial modeling on unemployment data are using the SAR and SEM model, and the BYM CARBayes model is usually used for disease mapping. In this research, use the BYM CARBayes model on the data of unemployment in East Java. Furthermore, the fitted number of unemployment is used to map each region in East Java with LISA analysis. The mapping of each region is used to determine the region that has an outlier of unemployment with the number of spatial auto correlation is calculated using Moran’I analysis. The modeled data using the BYM CARBayes give the result that the total population and percentage of high school graduates affect the number of unemployment significantly and negatively. The number of the working-age population, GDP, and the minimum wages in each region are affecting the number of unemployment significantly and positively. The map of each region in East Java using the number of unemployment gives 3 regions with an outlier of unemployment. There are Sidoarjo regency, Gresik Regency, and Surabaya city.