Pemodelan Data IPM Papua Dengan Regresi Terboboti Geografis - Halim, Fransiska Atrik; Gunardi (Jurnal Ilmiah Soulmath : Jurnal Edukasi Pendidikan Matematika, 2023)
Geographically weighted regression (GWR) is the development of global linear regression for data containing spatial heterogeneity. The model obtained from the geographically weighted regression is different for each location of observation. The purpose of this study is to explain the procedures in geographically weighted regression, apply geographically weighted regression in modeling human development index (HDI) data in Papua in 2019, and identify factors that have a significant effect on HDI in each regency/city in Papua in 2019. Based on the analysis, the following results are obtained, (1) The best model for HDI data in Papua 2019 based on the smallest AIC value is the model with the weighting function fixed Gaussian, (2) Geographically weighted regression is better than multiple linear regression in modeling HDI data in Papua 2019, (3) Variables that significantly influence HDI in Papua 2019 differ in each regency/city where HDI in 25 of 29 regencies/cities is influenced by high school participation rates and per capita expenditure, while HDI in 4 regency/city are influenced by the percentage of the poor population, high school participation rates, and per capita expenditure.