Visual exploration of data enables users and analysts observe interesting patterns that can trigger new research for further investigation. 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 because 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 multivisualization approaches and mashups. We developed ELBAR explorer that visualizes a vast amount of scientific observational data about the Brazilian Amazon Rainforest. The core contribution is a novel mechanism for animating between the different observed values, thus illustrating the observed changes themselves.
ELBAR Explorer was demonstrated at ISWC2014 in October, 2014. ELBAR is used in blended learning courses as a case tool to explore Linked Spatial Data.
The following paper provides more details about ELBAR:
- Suvodeep Mazumdar and Tomi Kauppinen. Visualizing and Animating Large-scale Spatiotemporal Data with ELBAR Explorer. In Proceedings of the ISWC 2014 Posters & Demonstrations Track, a track within the 13th International Semantic Web Conference (ISWC 2014), Riva del Garda, Trentino, Italy, October, 2014. [BibTeX]
The following video is used for accompanying material to show the main functions of ELBAR: