Understanding the Spatial Distribution of Stunting in East Java, Indonesia: A Comparison of GWR and MS-GWR Models

Abstract

Stunting is a growth impairment condition in children under five years old, resulting from chronic malnutrition and repeated infections, which causes them to be shorter than expected for their age. East Java is one of twelve priority provinces, with a stunting prevalence of 17.7% in 2023. Accurate identification of the factor influencing stunting is essential to support effective and targeted interventions. Given the spatial variability in these factors, conventional regression models such as Ordinary Least Squares (OLS) are inadequate. Geographically Weighted Regression (GWR) addresses this by allowing local variation, yet it assumes a uniform spatial scale across variables. This study employs the Multiscale Geographically Weighted Regression (MS-GWR) model, which enables each explanatory variable to operate at its own optimal spatial scale. The results show that MS-GWR with an adaptive Gaussian weighting function provides the best fit, with an AICc of 67.7426 and an R2 is 0.79. Seven variable groups significantly influence stunting, including exclusive breastfeeding, early initiation of breastfeeding (EIB), and upper respiratory tract infections (URTIs), as well as combinations of these factors. These findings highlight the importance of formulating location-specific and context-sensitive policies that reflect the dominant characteristics of each region to effectively and sustainably accelerate stunting reduction.