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”).
  • OU Rise library analytics viz

    1. 1. Visualising Activity Data<br />Tony Hirst<br />Dept of Communication and Systems,<br />The Open University<br />Scattered puzzle pieces next to solved fragment by HoriaVarlan<br />
    2. 2. Visual Analysisvs.Presentation Graphics<br />
    3. 3. This is NOT a presentation about:<br /><ul><li> data discovery
    4. 4. data preparation
    5. 5. data cleansing</li></li></ul><li>
    6. 6. 0<br />What data is there?<br />
    7. 7. AEIOU – Aberystwyth University – aggregated repository activity data<br />Agtivity– University of Manchester – usage data from Advanced Video Conferencing services users<br />Exposing VLE Data – University of Cambridge – activity and attention data for Cambridge’s institutional virtual learning environment<br />Library Impact Data – Huddersfield University – prove a statistically significant correlation between library usage and student attainment<br />RISE – Open University – use attention data recorded by EZProxy to provide recommendations to users of the EBSCO Discovery search solution.<br />Salt – University of Manchester – using 10 years of library circulation data to support long tail discovery<br />Shared OpenURL Data – EDINA – open OpenURL data<br />STAR-Trak– Leeds Metropolitan University – highlight and manage interventions with students who are at risk of dropping out<br />UCIAD – Open University – investigate the use of semantic technologies for integrating user activity data from different systems<br />
    8. 8. Some example visualisation types…<br />
    9. 9.
    10. 10. How manydimensions?<br />
    11. 11. Q:<br />So what might aTREEMAPbe good for?<br />
    12. 12. aka “seasonal subseries”<br />
    13. 13. Q:<br />So what might aCYCLE PLOTbe good for?<br />
    14. 14. gnuplot<br />Use white space<br />
    15. 15. Q:<br />What else dotime series hide?<br />
    16. 16. matplotlib<br />Trends<br />#time series data in d<br />#first difference<br />fd=np.diff(d)<br />Autocorrelation<br />
    17. 17. Gephi<br />
    18. 18.
    19. 19.
    20. 20. How manydimensions?<br />
    21. 21.
    22. 22.
    23. 23. Q:<br />So where might we findNETWORK GRAPHSdefined?<br />
    24. 24.
    25. 25. Tools…<br />
    26. 26. …vary…<br />
    27. 27. (ggplot)<br />R<br />
    28. 28. Protovis<br />
    29. 29. Processing<br />
    30. 30.
    31. 31. Data<br />Application<br />Output<br />Data<br />[Code]<br />Output<br />
    32. 32. Tools can also be appropriated…<br />
    33. 33. Gource<br />
    34. 34.
    35. 35. Things to remember…<br />
    36. 36. Data has “shape”<br />“Shape Sorter”, by Ella’s Dad<br />
    37. 37.
    38. 38. 1<br />What size/shape/state is it in…?<br />
    39. 39. Big text file..?…ask your nearest Unix sysadmin for help<br />http://blog.ouseful.info/2011/06/03/postcards-from-a-text-processing-excursion/<br />http://bit.ly/lOVySX<br />
    40. 40. 2<br />How is it structured?<br />
    41. 41.
    42. 42. Bulk properties andsimple manipulations<br />Number of rows containing x<br />Structural ordering (sort by column)<br />…again, ask your nearest Unix sysadmin for help<br />
    43. 43. Data format can be key<br />“Key”, by dewitt<br />
    44. 44. 3<br />How is it formatted?<br />
    45. 45.
    46. 46. 4<br />“Writing diagrams”<br />
    47. 47. Graphviz<br />
    48. 48. .dot<br />digraph test {<br />CSV [shape=box]<br />KML [shape=box]<br />JSON [shape=box]<br />XML [shape=box]<br />RDF [shape=box]<br />HTML [shape=box]<br />GoogleSpreadsheet [shape=Msquare]<br />RDFTripleStore [shape=Msquare]<br />"[SPARQL]" [shape=diamond]<br />"[YQL]" [shape=diamond]<br />"[GoogleVizDataAPI]" [shape=diamond]<br />"<GoogleGadgets>" [shape=doubleoctagon]<br />"<GoogleVizDataCharts>" [shape=doubleoctagon]<br />"<GoogleMaps>" [shape=doubleoctagon]<br />"<GoogleEarth>" [shape=doubleoctagon]<br />"<JQueryCharts_etc>" [shape=doubleoctagon]<br />…<br />…<br />"[SPARQL]"->RDF;<br />"[SPARQL]"->XML;<br />"[SPARQL]"->CSV;<br />"[SPARQL]"->JSON;<br />JSON-> "<JQueryCharts_etc>";<br />CSV->"{GoogleRefine}"<br />CSV->ScraperWiki<br />JSON->ScraperWiki<br />"[YQL]"->ScraperWiki<br />ScraperWiki->CSV<br />HTML->ScraperWiki<br />HTML->"[YQL]"<br />"[SPARQL]"->"[YQL]"<br />"{GoogleRefine}"->CSV [style=dashed]<br />CSV->"<Gephi>" [style=dashed]<br />"<Gephi>"->CSV [style=dashed]<br />RDF->"[YQL]”<br />}<br />
    49. 49. 5<br />Once only/first use?N times use?Automation?<br />
    50. 50. Data<br />Application<br />Output<br />Data<br />[Code]<br />Output<br />
    51. 51. 6<br />What do you wantto learn from it?<br />
    52. 52. How canvisual(isation)s help?<br />
    53. 53. Just remember this:what stories- are you hoping to discover?- are you trying to tell?<br />
    54. 54. I hope that’s beenouseful.info….?<br />
    55. 55. Treemap Caveat – Stephen Few<br />

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