Application of Geographically Weighted Regression Method on the Human Development Index of Central Java Province - Hasibuan, Devi Octaviani; Pau Teku, Heribertin; Drostela Putri, Maria Fatima; Setyawan, Yudi; Dwi Bekti, Rokhana (Enthusiastic : International Journal of Applied Statistics and Data Science, 2023)
Spatial data are data containing information on the location or geography of a region on the representation of objects on earth. Geographically Weighted Regression (GWR) is a development of the Ordinary Least Square (OLS) theory into a weighted regression model that considers spatial effects, resulting in a parameter estimation that can only be used to predict each location where the data are observed.  The Human Development Index (HDI) is an essential indicator for measuring success in efforts to build human quality of life. HDI data regencies/cities in Central Java are interconnected, so it is said to be spatial data and there are spatial effects in it. Therefore, the GWR method was applied to obtain faculties affecting HDI in Central Java Province. The data used were secondary data in 2020.  The determination coefficients of the GWR model ranged between 76.09% and 87.16%. If the variable values of population density and Gross Regional Domestic Product (GRDP) increase by one unit in each district/city in Central Java Province, the HDI variable value increases. These results were visualized on a dashboard providing information about the characteristics of HDI and independent variables, GWR parameter estimates, and the significance of independent variables in each regency/city.