VIsualization environment for coastal analysis (VINCA)
VINCA (VIsualization environment for coastal analysis) is a multiple coordinated-view system, developed using Java and the Processing.org libraries and designed for the exploration of unstructured oceanographic datasets. The main activity for this project was the design, administration and analysis of a questionnaire-based evaluation study for the usability assessment of VINCA.
R. L. S. F. George, P. E. Robins, A. G. Davies, P. D. Ritsos, and J. C. Roberts, “Interactive visual analytics of hydrodynamic flux for the coastal zone,” Environmental Earth Sciences, vol. 72, no. 10, pp. 3753–3766, Nov. 2014.
Researchers wish to study the potential impact of sea level rise from climate change, and visual analytic tools can allow scientists to visually examine and explore different possible scenarios from simulation runs. In particular, hydrodynamic flux is calculated to understand the net movement of water; but typically this calculation is tedious and is not easily achieved with traditional visualization and analytic tools. We present a visual analytic method that incorporates a transect profiler and flux calculator. The analytic software is incorporated into our visual analytics tool Vinca, and generates multiple transects, which can be visualized and analysed in several alternative visualizations; users can choose specific transects to compare against real-world data; users can explore how flux changes within a domain. In addition, we report how ocean scientists have used our tool to display multiple-view views of their data and analyse hydrodynamic flux for the coastal zone.
R. L. S. F. George, P. D. Ritsos, and J. C. Roberts, “Interactive Oceanographic Visualization using spatially-aggregated Parallel Coordinate Plots,” in Posters presented at EuroVis 2014, June 9-13 , Swansea, Wales, UK, 2014.
Visual Analytics interfaces allow ocean scientists to interactively investigate and compare different runs and parameterizations. However, oceanographic models are complex, temporal and the datasets that are generated are huge. Parallel Coordinate Plots can help explore multivariate data such as ocean-science data. Common issues with traditional PCPs of clutter and performance inhibit interactive spatial exploration. We describe techniques that aggregates the PCP based on the spatial nature of the data and we render the polylines as ranges.