Pemodelan Indeks Pembangunan Manusia (IPM) Metode Baru Menurut Provinsi Tahun 2015 Menggunakan Geographically Weighted Regression (GWR)

Abstract

The Human Development Index (HDI) is a parameter of quality of life for an area. The HDI explains how residents can access the results of development in obtaining income, health and education. One method that can be used to find out the factors that influence the human development index in modeling is regression analysis of ordinary least square (OLS). In the Human Development Index data, there is a dependency between measuring data and the location of a region. Therefore, spatial regression analysis can be used in this study. The local form of spatial regression analysis is geographically weighted regression (GWR). GWR shows the existence of spatial heterogeneity (location). This study compares between OLS regression and GWR in the new human development index method by province in 2015. In the GWR model we use fixed Gaussian kernel and kernel fixed bisquare as weighted function. The optimal bandwidth value is obtained by minimizing the cross validation (CV) and Akaike information criterion (AIC) coefficients. The results showed that the GWR model with Gaussian kernel function is better than GWR with bisquare kernel function and OLS model.

Keywords: human development index, ordinary least square, geographically weighted regression, kernel fixed Gaussian,  kernel fixed bisquare