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Dabbling with Data Visualisation

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There are a number of examples throughout history where visualisations have been used to explore or explain problems. Notable examples include Florence Nightingale's 'Mortality of the British Army' and John Snow's Cholera Map of London. Recently the increased availability of data and software for analyzing and generating various views on this data has made it easier to generate data visualisations. In this presentation Martin Hawksey, advisor at the Jisc Centre for Educational, Technology and Interoperability Standards (Cetis), will demonstrate simple techniques for generating data visualisations: using tools (including MS Excel and Google Spreadsheets), drawing packages (including Illustrator and Inkscape) and software libraries (including d3.js and timeline.js). As part of this participants will be introduced to basic visual theories and the concepts of exploratory and explanatory analytics. The presentation will also highlight some of the skills required for discovering and reshaping data sources.

The presentation was live-blogged by Nicola Osborne (EDINA) http://nicolaosborne.blogs.edina.ac.uk/2013/06/19/data-visualisation-talk-by-martin-hawksey/

The slides contain links to source (when you get to the data/vis matrix some of the thumbnails are live links), here’s also the bundle of top level links http://bitly.com/bundles/mhawksey/l

[Note: Only images/text after slide 13 Making Data Visualisation (unless attributed to other authors is CC-BY Martin Hawksey]

Published in: Education, Technology
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Dabbling with Data Visualisation

  1. 1. Data VisualisationMartin Hawksey (@mhawksey)http://bit.ly/DataVisEd
  2. 2. http://bit.ly/DataVisEdhttp://www.guardian.co.uk/news/datablog/2013/mar/15/john-snow-cholera-map
  3. 3. WHAT CAN WE LEARN FROM JOHNSNOW’S CHOLERA MAP
  4. 4. Bertin’s Visual Variables
  5. 5. Gestalt psychology(Gestalt Laws)
  6. 6. 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.
  7. 7. http://bit.ly/DataVisEd
  8. 8. MAKING VISUALISATIONS
  9. 9. Use DrawD3CodeHT Bret Victor
  10. 10. MAKING VISUALISATIONS
  11. 11. Dr. Tony HirstThe Open University@psychemediaouseful.infoLuke, find the feed…
  12. 12. http://bit.ly/DataVisEd
  13. 13. Use Draw CodeAccessShapeDisplayTwitter (Topsy) Media TimelineCaution: APIs change and break things
  14. 14. Use Draw CodeAccessShapeDisplayAudioboo Google MapTip: API documentation is often incomplete.Sometimes it’s worth a punt
  15. 15. Use Draw CodeAccessShapeDisplayAudioboo Google EarthCaution: Google Spreadsheets are a greatway to reshape data and republish butworth making a ‘flat’ copy in case the chainbreaks
  16. 16. Use Draw CodeAccessShapeDisplayNodeXL (SNA)Tip: NodeXL is a great tool to startlearning about social network analysisbecause it comes with a number of dataimporters. Also check nodexlgraphgalleryfor ideas and recipes
  17. 17. Use Draw CodeAccessShapeDisplayGephiCaution: Gephi is a great exploratory tool,but be careful when publishing flat imagesto explain the story the data tells you
  18. 18. Use Draw CodeAccessShapeDisplayHeart of #UKOER
  19. 19. Use Draw CodeAccessShapeDisplayTwitter Referral/Google AnalyticsCaution: This example is about to break dueto Twitter API changes 
  20. 20. Martin Hawkseymashe.hawksey.info@mhawksey

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