Geographically Weighted Regression Model in the Case of Unemployment in North Sumatra - Muhammad Arfie Munawar; Fibri Rahkmawati; Rini Halila Nasution (Jurusan Matematika FMIPA UNM, 2026)
Unemployment is a major and complex issue that affects many aspects of society, particularly in regions such as North Sumatra. This issue is not merely about numbers it also concerns the welfare of the population. Each district or city exhibits varying levels of unemployment; some areas have high rates, while others are relatively low. These variations reflect a clear spatial heterogeneity. To address the significant spatial variation in the factors contributing to unemployment, this study applies the Geographically Weighted Regression (GWR) model to analyze and model unemployment in North Sumatra, taking into account the spatial variability of each independent variable’s influence. GWR is a regression method that allows model parameters to vary across geographic locations, making it possible to capture non-uniform relationships at different spatial points. The methodology involves four weighting functions adaptive Gaussian, adaptive bisquare, fixed Gaussian, and fixed bisquare to identify the most optimal model. The best-performing GWR model is then constructed, and the spatial distribution patterns of unemployment are analyzed. The data used in this study are sourced from official statistics. The results show that the adaptive bisquare GWR model provides the best performance, yielding the lowest AIC value of 130.066. Variables such as population density and population growth rate are significant in most regions. However, number of industries is only significant in certain areas, while total population and minimum wage are not significant. These findings indicate that the factors driving unemployment and their spatial distribution vary across regions, highlighting the importance of considering spatial heterogeneity.