Incremental Spatio Temporal Clustering Application on Hotspot Data

Abstract

Hotspot is one of indications for forest and land fires. Analysis of hotspot data needs to be done as an early warning activity to prevent the occurrence of forest and land fires. The previous study analyzed hotspot clusters using the incremental spatio-temporal density-based clustering (ST-DBSCAN) algorithm. Hotspots in a cluster are considered as strong indicator for forest and land fires. However, clustering of hotspots is implemented on the command line interface. Through this interface, users are required to execute manually the commands to perform the clustering task on the dataset. The disadvantage of command line interface is that the commands have to be typed precisely and mistypes will cause errors.Therefore, this study aims to build a web-based application for clustering hotspot data using the incremental ST-DBSCAN module. This study uses hotspot dataset in the period 2014 to 2017. The application was developed using R programming language and Shiny framework. The method of Adaptive Software Development (ASD) was adopted in this study. The main functions of the application are incremental clustering on hotspot data and visualization of clustering result. The testing using the black box approach show that all features of the application work properly. Based on the result of usability assessment, the user satisfaction level reach 78.45% meaning that the application is quite easy to be used.