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London 2013 rgs_arrowsmith

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Visualising large cinematic data

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London 2013 rgs_arrowsmith

  1. 1. Visualising heterogeneous cinema data sets Big, Open Data and the Practice of GIScience RGS-IBG Annual Conference, London 29 August 2013 Colin Arrowsmith, School of Mathematical and Geospatial Science, RMIT University, Melbourne, Victoria, Australia Deb Verhoeven and Alwyn Davidson, School of Communication and Creative Arts, Deakin University, Melbourne, Victoria, Australia
  2. 2. A big data project “Only at the movies: Kinomatics” School of Mathematical and Geospatial Sciences 2
  3. 3. Objective To investigate spatial patterns of film diffusion across the world. – How do films circulate around the world? – Does spatial clustering affect film screening? – How does seasonality affect screening? School of Mathematical and Geospatial Sciences 3
  4. 4. Dimensions of “Big data” • Variety • Velocity • Volume IBM “Bringing big data to the Enterprise” (http://www-01.ibm.com/software/au/data/bigdata/) • Visualization School of Mathematical and Geospatial Sciences 4
  5. 5. Working with “Big data” • Database downloaded from commercial film data collector • 2 to 2.5 million showtime records per week • 30000 movies downloaded after seven months • 28000 cinema venues and 118000 screens • 63.5 million records equating to 4.8 Gbytes of data School of Mathematical and Geospatial Sciences 5
  6. 6. Database schema School of Mathematical and Geospatial Sciences 6
  7. 7. Projects exploring approaches for visualising and analysing big film data • Geographic methods – Post-war cinema venues in Australia (change-over-time) – Global cartograms for cinema (point-in-time) – Global patterns of movement • Non-geographic (conceptual) – Multivariate visualisations (change-over-time) – Film circulation (Markov-Chains) School of Mathematical and Geospatial Sciences 7
  8. 8. Geographic examples • Post-war cinema venues in Australia (change-over-time) • Global cartograms for cinema (point-in-time) • Global patterns School of Mathematical and Geospatial Sciences 8
  9. 9. Static maps of post war cinema venues in Australia • Basis for data was scanned “Film Weekly” summaries • Base year of 1948 derived • New and closed cinemas determined • Significant post-processing School of Mathematical and Geospatial Sciences 9
  10. 10. Film Weekly scan School of Mathematical and Geospatial Sciences 10
  11. 11. Rural scale changes 1948 to 1953 1963 to 1968 1953 to 1958 1958 to 1963 1968 to 1971 School of Mathematical and Geospatial Sciences 11
  12. 12. Rural scale changes 1948 to 1953 1963 to 1968 1953 to 1958 1958 to 1963 1968 to 1971 School of Mathematical and Geospatial Sciences 12
  13. 13. Urban scale changes (Melbourne) 1948 to 1953 1963 to 1968 1953 to 1958 1958 to 1963 1968 to 1971 School of Mathematical and Geospatial Sciences 13
  14. 14. Global cinema cartograms • Cartogram is a map where a thematic variable is substituted for area (or distance) • Population substituted for area School of Mathematical and Geospatial Sciences 14
  15. 15. Cartograms Global cinema numbers 15
  16. 16. Global screen numbers 16
  17. 17. Continent-wide patterns School of Mathematical and Geospatial Sciences 17
  18. 18. Global patterns School of Mathematical and Geospatial Sciences 18
  19. 19. Life of Pi 30 November 2012 7 December 2012 14 December 2012 21 December 2012 19
  20. 20. Life of Pi 28 December 2012 11 January 2013 4 January 2013 17 January 2013 20
  21. 21. Life of Pi (November 2012 to January 2013) School of Mathematical and Geospatial Sciences 21
  22. 22. Life of Pi (November 2012 to January 2013) School of Mathematical and Geospatial Sciences 22
  23. 23. Non-geographic examples • Multivariate visualisations (change-over-time) • Film circulation (Markov-Chains) School of Mathematical and Geospatial Sciences 23
  24. 24. 24
  25. 25. Visualisations School of Mathematical and Geospatial Sciences 25
  26. 26. Movement approaches: The Greek cinema circuit • Objective – To explore historical changes in the diasporic Greek cinema distribution of Finos and Anzervos films during the period 1956 to 1963 • Rationale – To demonstrate the role of geographic analysis in understanding cinema circuit behaviour School of Mathematical and Geospatial Sciences 26
  27. 27. Data acquisition • Archival newspaper and oral history research • Government records – censorship records – theatre licence and company records • Geo-location using street address or via GPS School of Mathematical and Geospatial Sciences 27
  28. 28. Anzervos School of Mathematical and Geospatial Sciences 28
  29. 29. Finos School of Mathematical and Geospatial Sciences 29
  30. 30. Anzervos (section) School of Mathematical and Geospatial Sciences 30
  31. 31. Finos (section) School of Mathematical and Geospatial Sciences 31
  32. 32. Key chains identified No. of venues Anzervos Finos 1 B C B A 2 BC CB BC AD 3 BCB CBC BCB BCA 4 BCBC MGPC BCBC BCBA School of Mathematical and Geospatial Sciences 32
  33. 33. Circos – circular visualisations • Film sequence (Fort of Freedom): – BCBBBBBCAABBBBBB by screening or – BCBCAB venue sequencing School of Mathematical and Geospatial Sciences 33
  34. 34. Change in sequence (Anzervos) Ali Pasha and Mrs Frossini The Fort of Freedom School of Mathematical and Geospatial Sciences 34
  35. 35. Change in sequence (Finos) Music, Povery and Pride Astero School of Mathematical and Geospatial Sciences 35
  36. 36. Change of venue date School of Mathematical and Geospatial Sciences 36
  37. 37. Change of venue date Ali Pasha and Mrs Frosini 3.5 A J S 3 Months 2.5 2 1.5 C CA A 1 0.5 BCBBB BB C 0 0 10 20 30 40 50 60 70 Days The Fort of Freedom 35 A B B B B B 30 Months 25 20 15 10 5 B C B B B B B C A 0 0 5 10 15 20 25 30 35 40 Days School of Mathematical and Geospatial Sciences 37
  38. 38. Change of venue date Music, Poverty and Pride 100 F 90 BBBB BBD DD A D K K 80 70 BBBBB Months 60 50 40 30 20 10 G P II A C 0 0 20 40 60 80 100 120 Days Astero 35 JJJ JJJ F K D 30 Months 25 20 BB B BB B 15 O 10 5 B B B BBC CB B B B BA B A A O D BBB BB A 0 0 50 100 150 200 250 Days School of Mathematical and Geospatial Sciences 38
  39. 39. OLIVE TREES • The olives are where films finished: green= Sydney venue, purple = Melbourne venue • Leaves are screenings: yellow is QLD, light green is NSW, darker green is VIC, dark brown is SA • The distance is days between screenings and done to scale Anzervos Finos School of Mathematical and Geospatial Sciences 39
  40. 40. Issues working with “big” complex cinema data •Multiple sources of data •Working at multiple scales •Working with historic data •Multiple definitions •Need for visualising both geographic and conceptual relationships School of Mathematical and Geospatial Sciences 40

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