Measuring and Profiling Social Vulnerability to Natural Disaster in Indonesia in 2019
DOI:
https://doi.org/10.20956/j.v19i1.21686Keywords:
Indonesia, K-Means Clustering, Natural Disasters, Social Vulnerability, SoVIAbstract
Nowadays, natural hazards are often seen from the nature perspective only. However, it is necessary to know not only about the hazards, but also the community resilience to prepare for, respond to, and recover from disasters based on the social characteristics which are called social vulnerability. This study provides the identification of social vulnerability to natural hazards condition and characterization of the dominant factors at the district level in Indonesia using secondary data. The principal component analysis (PCA) is used to reduce 13 district-level variables into 4 components that represents the driving factors of social vulnerability. The results of PCA are used to quantify the social vulnerability level of the districts in Indonesia using social vulnerability index (SoVI), followed by the deeper exploration of social vulnerability problem using K-Means Clustering. The SoVI and cluster results were mapped by using QGIS to identify the social vulnerability at districts level. The research shows that most districts in Indonesia are at a low-level vulnerability. The districts with low vulnerability are spread in the Sumatera and Kalimantan area. However, there are 43 Districts in Eastern Indonesia are in a high-level vulnerability. These districts also suffer many problems, such low sosioeconomic status. The results of this study support not only the previous social vulnerability studies but also the government as the policymakers by setting priority regions and allocating the policies according to main social vulnerability problem of each district, especially in the most vulnerable regions.
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