Evaluating Kernel Weighting Functions in Geographically Weighted Logistic Regression for Spatial Modelling of Stunting in East Lombok

Siti Hariati Hastuti; Alissa Chintyana; Hanipar Mahyulis Sastriana

doi:10.37905/euler.v13i3.33951

Evaluating Kernel Weighting Functions in Geographically Weighted Logistic Regression for Spatial Modelling of Stunting in East Lombok

Siti Hariati Hastuti, Alissa Chintyana, Hanipar Mahyulis Sastriana

Abstract

Stunting remains a major public health concern in Indonesia, with East Lombok Regency recording the highest prevalence in West Nusa Tenggara Province in 2022. This study aims to identify factors influencing stunting while accounting for spatial heterogeneity across regions. The Geographically Weighted Logistic Regression (GWLR) method was applied, comparing three kernel weighting functions: Fixed Gaussian, Adaptive Gaussian, and Adaptive Bisquare, to determine the best-fitting model. Parameter estimation was conducted using Maximum Likelihood Estimation with the Newton–Raphson iterative procedure. The results show that the Adaptive Gaussian kernel provided the best model performance, indicated by the lowest Corrected Akaike Information Criterion (AICc) value of 28.346. Spatial mapping identified two regional clusters: one where vitamin A supplementation significantly affected stunting, and another where no explanatory variables were significant. These findings emphasize the importance of incorporating spatial effects in public health modeling to support more targeted and context-specific interventions for stunting reduction at the local level.

Keywords

East Lombok; GWLR; kernel function; spatial heterogeneity; stunting

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References

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DOI: https://doi.org/10.37905/euler.v13i3.33951