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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?
  • Transcript

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

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