Geographical Weighted Regression of Risk Factor of Stunting in Malang Regency, Indonesia

Adipandang Yudono(1*), Joko Purnomo(2), Ratnaningsih Damayanti(3)

(1) Dept. of Urban and Regional Planning, Brawijaya University
(2) Government Science Study Programme, Faculty of Social and Political Science, Brawijaya University
(3) Government Science Study Programme, Faculty of Social and Political Science, Brawijaya University
(*) Corresponding Author


Stunting has become a global concern. The incidence of stunting in the world contributes to 15% of under-five mortality, with 55 million children losing their health, and it is estimated to reduce the country's GDP level by up to 7%. In Indonesia, the incidence of stunting has become one of the main health problems that need to be solved immediately. Malang Regency is one of the districts in East Java Province that has received the spotlight regarding this problem. This research examined the risk factors of stunting in Malang Regency through Geographically Weighted Regression (GWR). GWR was carried out to calculate the correlation between predetermined demographic, health, and economic variables, which were assumed to influence risk factors of stunting. GWR allocation and model examinations are important in understanding risk factors of stunting in the study of disease transmission in the investigation zone. Based on GWR analysis, the research shows that only four (4) sub-variables were significant: the number of poor people, level of education, number of health facilities, and access to health facilities. We also found that Lawang, Gondanglegi, and Turen districts have high-risk areas to stunting. Therefore, within this study that correlates to government policy to decrease or eliminate stunting incidents, districts belonging to the high-risk class should be prioritized or concerned. Moreover, based on LISA, some districts are affected by the risk factors of stunting from the surrounding districts with higher stunting potential value such as Gondanglegi and Pagelaran Districts.


Geographical Weighted Regression

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