Village classification index prediction using geographically weighted panel regression

Abstract

Abstract Village classification index is a certain status of the achievements of village development activities. In measuring the achievement of village classification index, it needs to be made in several time periods and must concern to the spatial effects because the geographical conditions of each village are diverse. It is necessary to study the variables that affect the village classification index in several time periods. Statistical methods that used in overcome the spatial effects of panel data type is Geographically Weighted Panel Regression (GWPR), which is a combination of Geographically Weighted Regression (GWR) models and panel data regression. This study focused on the establishment of GWPR model with fixed effects using fixed bisquare kernel on the village classification index in Batang Regency, 2015-2018. The results of this study indicate that the fixed effect model GWPR differ significantly on panel data regression model, and the model generated for each location will be different from one another. In addition, all independent variables namely the community economy, security and order, and community participation in development have a significant effect on the village classification index for all villages with R-square value of 0.3952.