Mixed Geographically and Temporally Weighted Regression Analysis of Percentage of Poverty in Java Island

Abstract

Regression analysis is a method that can be used to analyze the linear relationship between explanatory variables with a response variable to analyze problems. The problem of poverty is one of the fundamental problems and is the center of government attention in every country especially developing countries like Indonesia, precisely in Java Island that have the largest number of poor people. In poverty data, there is spatial and temporal diversity. Besides, potential differences between regions in Indonesia allow for the diversity of data and can be locally and globally addressed so that the MGTWR method can be used. In this study using data on the percentage of poor people in regency/municipality in Java in 2012-2018. MGTWR modeling results show that the GDRP, literacy numbers, and EYS variables are global variables. Whereas the last variable of education was completed by elementary school, per capita expenditure, Raskin recipient households, residents of the 15-64 age group, and MYS were local variables. Based on the Bisquare kernel function, the MGTWR model is the best model to modeling the percentage of poor people in regency/municipality in Java with the smallest AIC value of 198.75 and PseudoR2 the biggest is 72.92%. In MGTWR modeling, 5 regional groups are formed based on factors that influence simultaneously.