Visualizing and animating large-scale spatiotemporal data with ELBAR explorer
Suvodeep Mazumdar, Tomi Kauppinen · SHURA (Sheffield Hallam University Research Archive) (Sheffield Hallam University) · 2014
Abstract. Visual exploration of data enables users and analysts observe interesting patterns that can trigger new research for further investiga-tion. With the increasing availability of Linked Data, facilitating support for making sense of the data via visual exploration tools for hypothesis generation is critical. Time and space play important roles in this be-cause of their ability to illustrate dynamicity, from a spatial context. Yet, Linked Data visualization approaches typically have not made efficient use of time and space together, apart from typical rather static mul-tivisualization approaches and mashups. In this paper we demonstrate ELBAR explorer that visualizes a vast amount of scientific observational data about the Brazilian Amazon Rainforest. Our core contribution is a novel mechanism for animating between the different observed values, thus illustrating the observed changes themselves.