Visual Conversations


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Some examples of how you can have a visual chat with unknown data sets, and how to persuade them to tell you some of their hidden stories,,,

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  • Visual Conversations with Data Datasets often contain amyriad number of stories,but how can we best make sense of them? Maybe avisual conversation can help? 30 mins
  • Example of data powered storytelling in YXR175/TXR120 robotics activity
  • Brief explanation of chart and what the labels are.
  • Wheel diameter, actual distance travelled
  • Livescribes, process of creation of the rich picture… the diagramming is an active storytelling process that builds on itself amd has potentially many narrative threads
  • Also how you position marks on a canvas in relation to each other
  • Collaborative commentary
  • The top, blue strip shows the gear (1 to 7); the green strip shows the throttle pedal depression (0-100%), and the red strip shows the brake (0-100%). The light blue strip is a composite of the previous three strips. The whiter the pixel, the closer it is to 100% throttle in 7th gear with no braking.The bottom two traces show the longitudinal and lateral g-force respectively. For the longitudinal trace, red shows braking – being forced into the steering wheel; green shows acceleration – being forced back into your seat. You’ll see the greatest g-force under braking occurs when the brakes are slapped full on… (the red bits in the third and fifth traces line up). For the latitudinal g-force, the red shows the driving being flung to the left (i.e. right hand corner), the green shows them being pushed out to the right.
  • Emergent Social Positioning: origins: 1.5 degree egonet (how followers follow each other, how hashtaggers follow each other)- projection maps from followers to folk they commonly follow;-- projection maps from hashtaggers to folk they commonly follow- projection maps from friends to folk who commonly follow them
  • Visual Conversations

    1. 1. Visual Conversations with Data Tony Hirst Dept of Communication and Systems, The Open University
    2. 2. Figure 1 Sensor Tracesis a chart that’s designed to be read
    3. 3. IT TELLS A STORY
    5. 5.
    6. 6. Dataconversations
    7. 7. Conversationsaround what’s not there…
    8. 8. Presentation Graphics vs. Visual Analysis
    9. 9. Explanatory visualizationData visualizations that are used totransmit information or a point ofview from the designer to thereader. Explanatory visualizationstypically have a specific “story” orinformation that they are intendedto transmit.Exploratory visualizationData visualizations that are used bythe designer for self-informativepurposes to discover patterns,trends, or sub-problems in adataset. Exploratory visualizationstypically don’t have an already-known story.
    10. 10. Visual Analysis or Presentation Graphics?
    11. 11. AlgorithmicVisualisation
    12. 12. ggplot2 (R)d3.js (Javascript)
    13. 13. ggplot() +geom_linerange(data = d1,aes(x= car, ymin = ymin,ymax = ymax)) +geom_point(data = d2,aes(x= car, y= value,shape = variable),size = 2) +opts(title="F1 2011 Korea nRace SummaryChart",axis.text.x=theme_text(angle=-90, hjust=0)) +labs(x = NULL, y = "Position", shape = "")
    14. 14.
    15. 15. ExploitingStructure
    16. 16. Hierarchical data and treemaps - medalsPivot tables
    17. 17. Macroscopes
    18. 18. aka “seasonal subseries”
    19. 19. Show me the difference… Let me see the difference…
    20. 20. Can I see the difference..?
    21. 21. Where exactly..?
    22. 22. Emergent views of structural properties
    23. 23. [ Freemind ]
    24. 24. Have you had a visualconversation with any of YOUR data lately?
    25. 25.