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

Coastal areas are vulnerable to disasters such as tsunamis, floods, large waves, and hurricanes.Most studies on disasters in coastal areas are based on surveys for specific areas, but studies investigating disasters on a country-wide level are few.Applying data analytics to disaster management is critical to reducing the impact of disasters.This study aims to classify provinces based on disaster events, disaster preparedness, and response capacity in coastal villages through cluster analysis, principal component analysis (PCA), and a combination of PCA and cluster analysis.This secondary study applies data mining techniques to official statistics in Indonesia.Data mining was performed with 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 large cluster, named the low-intensity cluster.The low-intensity cluster has a higher percentage of coastal villages (25%) than the high-intensity cluster (10%).The results of the PCA are used to classify regions through geographic heat maps and scatter plots.Additionally, the combination of multiple principal component analysis and cluster analysis produced three clusters with 6 (18%), 10 (30%), and 17 (52%) provinces.However, the cluster model from cluster analysis alone provides a better separation between clusters than the combination of PCA and cluster analysis.Ultimately, cluster analysis and PCA can be used independently, and both methods are complementary to exploring regional classification.The results of this study recommend improvements in disaster preparedness and response for coastal villages, especially provinces with a high percentage of coastal villages.