Spatial Autoregressive Model to Factors Poverty Gap Index in West Java 2023

Abstract

Spatial analysis is the analysis of data with spatial effects. The spatial autoregressive is used when the effect of the dependent variable at one location is influenced by the value of the dependent variable at nearby or neighboring locations. The spatial autoregressive model is more appropriate to model the factors influencing the poverty depth index in West Java in 2023. Based on the Spatial Autoregressive modeling, the variables that influence the Poverty Depth Index in West Java are Population Density, Open Unemployment Rate, and economic growth. The SAR modeling produces a higher coefficient of determination compared to the linear model, which is 68.88% with an AIC value of 18.6149.