Geographically Weighted Regression with Kernel Weighted Function on Poverty Cases in West Java Province: Regresi Terboboti Geografis dengan Fungsi Pembobot Kernel pada Data Kemiskinan di Provinsi Jawa Barat - Winda Nurpadilah; I Made Sumertajaya; Muhamad Nur Aidi (Indonesian Journal of Statistics and Applications, 2021)
Spatial regression analysis is a form of regression model that considers spatial effects. Geographically weighted regression (GWR) is the spatial regression methods that can be used to deal with the problem of spatial diversity. This method generates local model parameter estimates for each observation location. The application of spatial statistics can be done in all areas such as the problem of poverty. Poverty can be influenced by factors of proximity between regions, so that in determining the poverty factor, the proximity factor of the region cannot be ignored. West Java Province is a province with the largest population, so this study aims to model the poverty data in West Java Province by incorporating spatial effects. The weighting function used for the GWR model is the function of the fixed and adaptive kernels. The analysis results show that the fixed exponential kernel function has the smallest cross validation (CV) value, so the weighting matrix used in the model is determined by the exponential kernel function. The largest Â value and the smallest AIC value are owned by the GWR model with an exponential kernel function. Based on the results obtained by the the ANOVA table to test GWR's global goodness, the GWR model is more effective than global regression. Therefore, the GWR model is the best model when it used in West Javaâ€™s poverty cases. The effect of each explanatory variable on the percentage of poverty varies in each district/city in West Java Province.