Visualization of spatial decision tree for predicting hotspot occurrence in land and forest in Rokan Hilir District Riau

Abstract

Visualization is an important issue in datamining to easy understand patterns extracted from dataset. This research applied the Bottom-Up Approach method to develop a visualization module for a spatial decision tree in a geographic information system. Spatial data used in this work consists of nine explanatory layers and one target layers. Explanatory layers include maximum daily temperature, daily precipitation, wind of speed, distance of nearest river, distance of nearest road, land cover, peatland type, peatland depth, income source. The target layer contains hotspot and non-hotspot points that occurred in 2008. The result is the visualization module of spatial decision tree that has three main features including mapping window, interactive window, tree node and tabular visualization for predicting hotspot occurrence.