Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
The parallel coordinate plot in action: design and use for geographic visualization
Computational Statistics & Data Analysis - Data visualization
How users interact with biodiversity information using TaxonTree
Proceedings of the working conference on Advanced visual interfaces
prefuse: a toolkit for interactive information visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Service-Oriented Architecture: Concepts, Technology, and Design
Service-Oriented Architecture: Concepts, Technology, and Design
Extending taxonomic visualisation to incorporate synonymy and structural markers
Information Visualization - Special issue: Bioinformatics visualization
IEEE Transactions on Visualization and Computer Graphics
Efficiently managing large-scale raster species distribution data in PostgreSQL
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Integrating WebGIS with service oriented rural information grid: experience sharing
Proceedings of the 4th International Conference on Theory and Practice of Electronic Governance
| Hi-index | 0.00 |
Exploratory analysis of large-scale species distribution data is essential to gain information and knowledge, stimulating hypotheses and seeking possible explanations of species distribution patterns. Geographical Information System (GIS) has played an important role in modeling and visualizing species distribution patterns for a single or a limited number of species. However, traditional GIS models do not take taxonomic components of species distribution data into consideration and are neither effective nor efficient in managing large-scale species distribution data. In this study, we propose to embed and extend GIS for large scale species distribution data analysis. We provide an integrated data model that seamlessly links geographical, taxonomic and environmental data related to species distribution data analysis. We then present LEEASP (a Linked Environment for Exploratory Analysis of large-scale Species Distribution data), a prototype that has been developed based on the integrated data model. LEEASP utilizes the state-of-the-art advanced visualization techniques and multiple view coordination techniques to visualize different data sources that are relevant to species distribution data analysis. The North America tree species distribution data and other related data are used as an example to demonstrate the feasibility of the realization of the proposed integrated data model and how LEEASP can help users explore the geographical-taxonomic-environmental relationships