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Cinema Studies is Big
It’s The Pictures That Got Small
Deb Verhoeven, Colin Arrowsmith, Bronwyn Coate, Alwyn Davidson
HOMER, June 2013 @bestqualitycrab
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Kinomatics
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Kinematics: the study of the geometry of motion
Kinomatics: the study of the industrial geometry of
motion pictures
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Kinomatics: Key questions (I)
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• How can we use newly available, large scale systemic data to
determine and measure the critical factors affecting film industry
performance?
• What is the impact of new distribution and exhibition industry practices
resulting from the global adoption of digital projection technologies?
• How can we interpret the data in relation to Hollywood‟s increasingly
reliance on international markets for sustainability?
• What is the impact of environmental conditions on film diffusion?
• How might the techniques of New Cinema History enable Predictive
Analysis – based on real-time information analytics and fact-based
projections
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Kinomatics: Key questions (II)
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• The idea of data as both a subject and object of analysis
in cinema studies
• Issues of methodology and the kind of knowledge claims
that come with data-driven analysis
• The rhetoric of “problems and solutions” in which data is
mobilized
• Questions of agency and automation
• Confidentiality and the socialisation of data
• Questions around standards for development and
implementation
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Kinomatics: Global Showtime Project
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The team: Deb Verhoeven (Deakin University)
Colin Arrowsmith (RMIT University)
Bronwyn Coate (Deakin University)
Alwyn Davidson (Deakin University)
Steve Bennett (VeRSI)
Christopher McAvaney (Deakin
University)
ARC DP120101940: Only at the Movies?
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Kinomatics: Global Showtime Project
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The data: December 2012 to December 2013
All screenings of all movies for 48
countries
Collected by one company
Mode of collection differs
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Kinomatics: Global Showtime Project
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The specs: Compressed data files are
automatically downloaded via a
PERL synchronisation service
The project database is RHEL 6
standard MySQL 5.1.67
Stored on a virtual server using the
Deakin standard RedHat Enterprise
Linux (RHEL) version 6.4
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Kinomatics: Big Cinema Data
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Velocity
Veracity
Volume
Variety
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Kinomatics: Variety
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The
schema:
Distribution Movie Showtime Venue_Location
Movie_ID Movie_ID Movie_ID Venue_ID
Intl_Country Movie_Title Venue_ID Venue_Name
Intl_Title Parent_ID Show_Date Country
Intl_Rating Genre Show_Time Address
Intl_Release_Date Rating Special_Attributes City
Director Festival County
Movie_Genre Producer With_Movie_ID State
Movie_ID Actor Show_Sound Postcode
Movie_Genre Writer Show_Passes Lattitude
Distributor Show_Comments Longitude
Movie_Cast Release_Date Venue_Type
Movie_ID Release_Notes Extra_Venue
Cast_Name Running_Time Screens
Cast_Type Star_Rating Venue_Sound
URL Closed
Attributes
Seating
Comments
DMA_Market
Ticket_Adult
Ticket_Child
Ticket_Senior
Ticket_Online
Ticket_Bargain
Ticket_Comment
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Kinomatics: Volume
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One
week of
data =
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Kinomatics: Volume
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One
week of
data =
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Kinomatics: Volume
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One
week of
data =
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Kinomatics: Volume
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Les
Miserables
(red)
One week
of data =
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Kinomatics: Volume
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One
week of
data =
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Kinomatics: Volume
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One
week of
data =
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Kinomatics: Velocity
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Kinomatics: Veracity
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The data: Establishing trust in big data presents a
huge challenge as the variety and number of
sources grows.
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Kinomatics: Variables vs Comparables
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Predictive Modeling:
• Box Office Prophecy (Court, Plott, McKenzie)
http://apo.org.au/research/predictive-power-box-office-
prophecy-0
• Big data holds the promise of the use of advanced
mathematical, data mining, pattern recognition, and
machine learning techniques to analyze the past
performance of thousands of motion pictures, and
forecast movie revenues based on this analysis
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Kinomatics: More reading
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Verhoeven, Deb (2012) „New Cinema History and the Computational
Turn‟, Beyond Art, Beyond Humanities, Beyond Technology: A New
Creativity, World Congress of Communication and the Arts Conference
Proceedings, University of Minho, Portugal
Verhoeven, Deb (2013) „What is a Cinema? Death, Closure and the
Database‟, in Watching Films: New Perspectives on Movie-Going, Exhibition and
Reception (eds) Albert Moran and Karina Aveyard, Intellect Books, pp. 33-51.
deakin.academia.edu/DebVerhoeven
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I’m ready for my questions now
Deb Verhoeven, Colin Arrowsmith, Bronwyn Coate, Alwyn Davidson
HOMER, June 2013 @bestqualitycrab
Editor's Notes
Visualization: Steve Bennett6 weeks of data = mid December to end January (2013)The legend for the colour coding is as follows: [movie= "The Hobbit: An Unexpected Journey in 3D"] { marker-fill: blue; } [movie = "Skyfall"] { marker-fill: red; } [movie = "Rise of the Guardians"] { marker-fill: orange; } [movie = "Life of Pi"], [movie = "Life of Pi 3D"] { marker-fill: green; } [movie = "Populaire"] { marker-fill: pink; } [movie = "Talaash"] { marker-fill: yellow; marker-line-width: 1; } [movie = "The Twilight Saga: Breaking Dawn - Part 2"] { marker-fill: purple; }(Other movies are shown as blue-grey. In the US, a lot of these are Lincoln)
Does ‘big cinema data’ enable us to answer questions previously consideredbeyond reach?
For example 55-60% of data collected digitally and automatically – e.g. from point of sale/ticketing system etc.
AlsoVISALISATION?Real world big data applications commonly address one or two of the “V”s. However there are many organizations with big data projects that do indeed incorporate all three; these usually involve high volumes of streaming data from a variety of sources.
Big data: Its not the size of the data that counts – it is capacity (what you can do with it) that is its defining feature. Big data is really just “initially unmanageable” data.
Green =everythingRed = Les Mis
Screenings of Les Mis in one week.
Population of Australia = 23mPopulation of USA = 316mPopulation of Canada = 35mPopulation of UK = 52m
Worldwide Telescope
Multiplied in the humanities…?
For example 55-60% of data collected digitally and automatically – e.g. from point of sale/ticketing system etc.