Spatial Clustering Regression in Identifying Local Factors in Stunting Cases in Indonesia

Ummul Auliyah Syam; Anik Djuraidah; Utami Dyah Syafitri

doi:10.31764/jtam.v9i2.29803

Spatial Clustering Regression in Identifying Local Factors in Stunting Cases in Indonesia

Ummul Auliyah Syam, Anik Djuraidah, Utami Dyah Syafitri

Abstract

Stunting is a significant health problem in Indonesia with high spatial disparities between regions. This study applies the Spatial Clustering Regression (SCR) method to analyze spatial patterns and identify local factors influencing stunting. SCR is a method that combines spatial regression and clustering analysis simultaneously using a k-means clustering-based formulation and a penalty likelihood function motivated by the Potts model to encourage similar clustering in adjacent locations with regression parameter estimation done locally in areas that have similar characteristics. This quantitative study uses secondary data from the Central Bureau of Statistics in 2022 covering 510 districts/cities, with one response variable (percentage of stunting) and seven explanatory variables reflecting socioeconomic, health, and infrastructure conditions. The results show that SCR divides the region into four spatial clusters characterized by different local factors. Cluster 1 has the lowest percentage of stunting that is influenced by access to clean water, sanitation, and education, Cluster 2 by poverty rate, number of public health centers, access to clean water, and education, Cluster 3 by poverty and nutrition of pregnant women, and Cluster 4 is the most vulnerable area with the highest stunting rate with a significant influential factor which is access to sanitation. The SCR approach allows for easier and more in-depth interpretation of results than other spatial methods such as GWR, as it can capture complex spatial patterns in the form of regional clusterings. These results provide a strong basis for formulating region-specific intervention policies, such as poverty alleviation and sanitation improvement in Cluster 4, strengthening health services in Cluster 2, developing education and nutrition programs in Cluster 3, and maintaining and improving nutrition consumption in Cluster 1.

Keywords

Cluster Analysis; Geographically Weighted Regression; Spatial Analysis; Spatial Clustering Regression; Stunting.

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DOI: https://doi.org/10.31764/jtam.v9i2.29803