Mapping Food Insecurity: Spatial Modelling of Undernourishment Prevalence in Indonesia using Geographically Weighted Regression

Toha Saifudin; Nur Chamidah; Fidela Sahda Ilona Ramadhina; Ilham Maulana Al Hasri; Nadya Lovita Hana Trisa; Hanny Valida; Muhammad Daffa Bintang Setyawan

doi:10.31764/jtam.v9i4.32063

Mapping Food Insecurity: Spatial Modelling of Undernourishment Prevalence in Indonesia using Geographically Weighted Regression

Toha Saifudin, Nur Chamidah, Fidela Sahda Ilona Ramadhina, Ilham Maulana Al Hasri, Nadya Lovita Hana Trisa, Hanny Valida, Muhammad Daffa Bintang Setyawan

Abstract

Undernourishment is a major global issue, with significant impact observed in Indonesia. A method of assessing the prevalence of energy deficiency resulting from inadequate nutrition is through the Prevalence of Undernourishment (PoU) index. From 2019 to 2022, Indonesia's PoU increased gradually, reaching 10.21% in 2022, indicating growing undernourishment and unstable food availability. This study aims to utilize Geographically Weighted Regression (GWR) to identify and analyze the factors contributing to undernourishment. The data were obtained from the Central Bureau of Statistics (BPS) in 2024, covering 38 provinces in Indonesia. This study examined six factors: per capita spending, access to potable water, mean years of schooling, access to adequate sanitation, college participation rate, and mean food expenditure. The findings show that the GWR model outperformed the conventional model, demonstrating greater explanatory power by accounting for 96.1% of the spatial variation in undernourishment and achieving the lowest AIC value of 176.7052. These findings highlight the need for region-specific food security policies, particularly in eastern Indonesia. The results can inform targeted government interventions and guide future spatial econometric research on food security.

Keywords

Undernourishment; Geographically Weighted Regression; Prevalence of Undernourishment.

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DOI: https://doi.org/10.31764/jtam.v9i4.32063