Lincoln2013 feb


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  • Some scene setting about what I mean by “flow”…
  • Suppose we have a table of numerical data associated with placenames on something like Wikipedia. How do we knock up a quick map view of the data?
  • 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.
  • Do we have a hashtag for the workshop?
  • Lincoln2013 feb

    1. 1. Quick Tour:Data Journalism Tony Hirst Dept of Communication and Systems The Open University Visiting Senior Research Fellow, University of Lincoln
    2. 2. #ddj
    3. 3. A quick example…The “Lego” approach to data journalism
    4. 4. Google Yahoo! PipeWikipedia HTML Spreadsheet CSV Import CSV =importHTML Embedded <embed> Google Map KML object
    5. 5. #ddj in the wild…
    6. 6. Jan 2013
    7. 7. 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.
    8. 8. DATA helps youFIND the storyDATA helps youTELL the story
    9. 9. BUT first you need to learn how tolisten to the stories that data can tell
    10. 10. … vs. a poll by the Media Standards Trust
    11. 11. “folk commonly followed by folkusing the #newsrwhashtag at the ESP #newsrwstart of the December 2012 event”
    12. 12. Facebook Likes
    13. 13.
    14. 14. Charts can hide numbers, butnumbers can hide distributions
    15. 15. Numbers& Charts
    16. 16. Another example…
    17. 17. Company Director Director Director Director Company Company Company Company
    18. 18. So where’s the data?
    19. 19. Digging for data…
    20. 20. “Creating” Data
    21. 21. “Quick Charts”Cut and paste … just add data
    22. 22.
    23. 23. Plotting NetworksGephi
    24. 24. “folk commonly followed by folkusing the #newsrwhashtag at the ESP #newsrwstart of the December 2012 event”
    25. 25. ( Rstudio )
    26. 26. GoogleOpenRefi ne
    27. 27. Martin Hawksey/@mhawksey
    28. 28.
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