BencanaVis visualization and clustering of disaster readiness using K Means with R Shiny: A case study for Disaster, Medical Personnel and Health Facilities data at Province level in Indonesia

The open data movement has led us into immensely useful applications and innovations for decision making, both for individual citizen as well as government. This study aims to create a web application called BencanaVis which provide innovative visualization of disaster government open data using Shiny, a web framework from R programming language. The datasets being used are available from Indonesian National Disaster Management Authority agency (or BNPB), the official Indonesian Open Data government portal and the Indonesian National Statistical Bureau (or BPS) website. We create three types of scenarios or experiments for the dataset. After that, we normalize the data using min-max use normalization. Then, we employ PCA (principal component analysis) to reduce feature dimensionality. Furthermore, we apply K-Means clustering techniques and calculate the cluster validity using Sum of Square Error (SSE), Davis-Bouldin Index (DBI), Dunn Index, Connectivity Index and Silhouettes Index. The cluster member from optimal number of k are then being analyzed to create a score for disaster readiness. We shall analyze this disaster readiness using the scoring produced by weighting the attributes values with weights from the AHP methods. Furthermore, we provide two visualizations; they are 3D scatter plot and cluster distribution using leaflet library from R. There are two other visualizations provided in the web application use heatmap and streamgraph library. The heatmap visualization shows the pattern distribution of all attributes and streamgraph visualization which refers to stacked area chart shows the number of 21 types disaster which recorded from BNPB data in 16 years during the year 2000 - 2016.