MC3: Data visualizations
Visual analytics can be a powerful tool for sharing research outcomes and ideas with diverse audiences, with knowledge mobilization being one of the main objectives of MC3. They illustrate qualitative and quantitative information in graphics and figures that communicate complex patterns, relationships and contexts between scientific concepts and theoretical frameworks. Adding a spatial dimension to the presentation of data enables a richer understanding of concepts, ideas and theories by engaging our visual pattern recognition and spatial reasoning abilities (Risch, Kao, Poteet & Wu 2008). In addition, visuals reduce the complex cognitive requirements for processing information and enhance our ability for synthesizing data and gaining insights on its meaning and / or implications (Keim, Mansmann, Schneidewind, Thomas, & Ziegler, 2008).
A Picture of Electrical Energy Use in Canada
Click on the dates along the bottom to animate the scene.
emerging mc3 data (phase 2)
This visualization depicts the top topics between the first phase of MC3: Meeting the Climate Change Challenge case study interviews and the second phase, when we re-interviewed a sub-sample from the original interviewees. We used the ‘text mining’ methodology to analyze our data.
This visualization represents topic richness across both phases of the MC3 research. Topic richness measures the diversity of topics in each phase; the more topics that are discussed, the richer that phase is. This visualization has filtered the data so as to show only the top 0.5% of topics in each phase.