Hotspot sequential pattern visualization in peatland of Sumatera and Kalimantan using shiny framework

Abstract

Fires on peatland frequently occurred in Sumatra and Kalimantan. Fires on peatland can be identified by hotspot sequential patterns. Sequential pattern mining is one of data mining techniques that can be used to analyse hotspot sequential patterns. Sequential pattern discovery equivalent classes (SPADE) algorithm can be applied to extract hotspot sequential patterns. The objectives of this work are: 1) to obtain hotspot sequential pattern in Sumatra and Kalimantan in 2014 and 2015, and 2) to develop a web based application using Shiny framework that is available in R package for hotspot sequential pattern visualization in peatland of Sumatra and Kalimantan. Hotspot sequential patterns were obtained using minimum support of 0.01 with the focus of analysis is the hotspot sequences with length two or more events. This work generated as many 89 sequences with length 2 or more in Sumatra in 2014, 147 sequences in Sumatra in 2015, 48 sequences in Kalimantan in 2014, and 51 sequences in Kalimantan in 2015. Hotspot sequential patterns are visualized based on peatland's characteristics, weather, and social economy. The features in this web based application have been tested and the results show that all features work properly according to the test scenario.