Disaster Event, Preparedness, and Response in Indonesian Coastal Areas: Data Mining of Official Statistics

Abstract

Coastal areas are vulnerable to disasters such as tsunamis, floods, large waves, and hurricanes. Many studies on disasters in coastal areas were based on surveys for specific areas, but limited research explored the whole country. Applying data analytics for disaster management is critical to reducing the impact of disasters. This study aims to classify provinces based on disaster events and disaster preparedness and response capacity in coastal villages through cluster analysis, principal component analysis, and a combination of principal component analysis and cluster analysis. This secondary study applies data mining techniques to Indonesian official statistics. Data mining used the Python Scikit-learn and Tableau analytical software. The unit of analysis is all provinces of Indonesia as an archipelago country. The cluster analysis optimally produced two clusters with 6 (18%) and 27 (82%) provinces. The small cluster, named the high-intensity cluster, has a higher intensity of disaster events, preparedness, and response than the big one, named the low-intensity cluster. The big cluster has a higher percentage of coastal villages (25%) than the first (10%). The results of the principal component analysis were used to classify regions through geographic heat maps and scatter plots. Combining multiple principal component analysis and cluster analysis provides an alternative method to cluster analysis alone. The analysis produced three clusters with 6 (18%), 10 (30%), and 17 (52%) provinces. However, the cluster model from cluster analysis alone is better than the model from the combination of principal component analysis and cluster analysis. Therefore, cluster analysis and principal component analysis might be used independently, and both methods are complementary to exploring regional classification. The result of this study suggests an improvement in disaster preparedness and response for coastal villages, especially for provinces with a high percentage of coastal villages.