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OU Rise library analytics viz

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  • Change the basis… eg in OU, might consider different presentations (“years”) of the same course (“month”).
  • Transcript

    • 1. Visualising Activity Data
      Tony Hirst
      Dept of Communication and Systems,
      The Open University
      Scattered puzzle pieces next to solved fragment by HoriaVarlan
    • 2. Visual Analysisvs.Presentation Graphics
    • 3. This is NOT a presentation about:
      • data discovery
      • 4. data preparation
      • 5. data cleansing
    • 6. 0
      What data is there?
    • 7. AEIOU – Aberystwyth University – aggregated repository activity data
      Agtivity– University of Manchester – usage data from Advanced Video Conferencing services users
      Exposing VLE Data – University of Cambridge – activity and attention data for Cambridge’s institutional virtual learning environment
      Library Impact Data – Huddersfield University – prove a statistically significant correlation between library usage and student attainment
      RISE – Open University – use attention data recorded by EZProxy to provide recommendations to users of the EBSCO Discovery search solution.
      Salt – University of Manchester – using 10 years of library circulation data to support long tail discovery
      Shared OpenURL Data – EDINA – open OpenURL data
      STAR-Trak– Leeds Metropolitan University – highlight and manage interventions with students who are at risk of dropping out
      UCIAD – Open University – investigate the use of semantic technologies for integrating user activity data from different systems
    • 8. Some example visualisation types…
    • 9.
    • 10. How manydimensions?
    • 11. Q:
      So what might aTREEMAPbe good for?
    • 12. aka “seasonal subseries”
    • 13. Q:
      So what might aCYCLE PLOTbe good for?
    • 14. gnuplot
      Use white space
    • 15. Q:
      What else dotime series hide?
    • 16. matplotlib
      Trends
      #time series data in d
      #first difference
      fd=np.diff(d)
      Autocorrelation
    • 17. Gephi
    • 18.
    • 19.
    • 20. How manydimensions?
    • 21.
    • 22.
    • 23. Q:
      So where might we findNETWORK GRAPHSdefined?
    • 24.
    • 25. Tools…
    • 26. …vary…
    • 27. (ggplot)
      R
    • 28. Protovis
    • 29. Processing
    • 30.
    • 31. Data
      Application
      Output
      Data
      [Code]
      Output
    • 32. Tools can also be appropriated…
    • 33. Gource
    • 34.
    • 35. Things to remember…
    • 36. Data has “shape”
      “Shape Sorter”, by Ella’s Dad
    • 37.
    • 38. 1
      What size/shape/state is it in…?
    • 39. Big text file..?…ask your nearest Unix sysadmin for help
      http://blog.ouseful.info/2011/06/03/postcards-from-a-text-processing-excursion/
      http://bit.ly/lOVySX
    • 40. 2
      How is it structured?
    • 41.
    • 42. Bulk properties andsimple manipulations
      Number of rows containing x
      Structural ordering (sort by column)
      …again, ask your nearest Unix sysadmin for help
    • 43. Data format can be key
      “Key”, by dewitt
    • 44. 3
      How is it formatted?
    • 45.
    • 46. 4
      “Writing diagrams”
    • 47. Graphviz
    • 48. .dot
      digraph test {
      CSV [shape=box]
      KML [shape=box]
      JSON [shape=box]
      XML [shape=box]
      RDF [shape=box]
      HTML [shape=box]
      GoogleSpreadsheet [shape=Msquare]
      RDFTripleStore [shape=Msquare]
      "[SPARQL]" [shape=diamond]
      "[YQL]" [shape=diamond]
      "[GoogleVizDataAPI]" [shape=diamond]
      "<GoogleGadgets>" [shape=doubleoctagon]
      "<GoogleVizDataCharts>" [shape=doubleoctagon]
      "<GoogleMaps>" [shape=doubleoctagon]
      "<GoogleEarth>" [shape=doubleoctagon]
      "<JQueryCharts_etc>" [shape=doubleoctagon]


      "[SPARQL]"->RDF;
      "[SPARQL]"->XML;
      "[SPARQL]"->CSV;
      "[SPARQL]"->JSON;
      JSON-> "<JQueryCharts_etc>";
      CSV->"{GoogleRefine}"
      CSV->ScraperWiki
      JSON->ScraperWiki
      "[YQL]"->ScraperWiki
      ScraperWiki->CSV
      HTML->ScraperWiki
      HTML->"[YQL]"
      "[SPARQL]"->"[YQL]"
      "{GoogleRefine}"->CSV [style=dashed]
      CSV->"<Gephi>" [style=dashed]
      "<Gephi>"->CSV [style=dashed]
      RDF->"[YQL]”
      }
    • 49. 5
      Once only/first use?N times use?Automation?
    • 50. Data
      Application
      Output
      Data
      [Code]
      Output
    • 51. 6
      What do you wantto learn from it?
    • 52. How canvisual(isation)s help?
    • 53. Just remember this:what stories- are you hoping to discover?- are you trying to tell?
    • 54. I hope that’s beenouseful.info….?
    • 55. Treemap Caveat – Stephen Few