Application of the Mixed Geographically Weighted Regression Model to Identify Influencing Factors for Literacy Development Index of Indonesian Society's in 2022 - Zulhijrah Zulhijrah; Ruliana Ruliana; Aswi Aswi (Indonesian Journal of Applied Statistics, 2025)
The mixed geographically weighted regression (MGWR) method is a combination of a linear regression model and a geographically weighted regression (GWR) model. The MGWR model can produce parameter estimates that have global parameter estimates, and other parameters that have local parameters according to the observation location. This method can be used in epidemiological studies that are influenced by spatial heterogeneity. The aim of this research is to determine and model the factors that influence the Community Literacy Development Index (CLDI) in Indonesia based on MGWR modeling. The data used in this research is CLDI data in Indonesia in 2022 along with the factors that are thought to influence it. The results of this research indicate that the MGWR model outperforms both the linear regression and GWR models, as it yields the lowest Akaike information criterion (AIC) value and an ?² value of 96.54%. Based on the modeling results, several factors influencing CLDI were identified, including the percentage of libraries, the adequacy ratio of library collections, the average length of schooling, and the level of participation in organized learning. Keywords: Literacy; literacy development index; mixed geographically weighted regression; spatial