Creative digital industries mapping

‘Creative Digital Industries in Australia: innovation in quantitative and qualitative mapping’ is a Linkage-Project (LP0453652) which was folded into the CCI. Partners are the Australian government Department of Communications, Information and the Arts (DCITA) and the Australian Film Commission. The initial project was completed in 2007.

Over a three-year period from July 2004, the project was to achieve three principal objectives:

* Stage 1: to map the size, scope and structure of the creative industries in Australia;
* Stage 2: to test and measure the nature of creative inputs into service sectors such as education, health, government and business services;
* Stage 3: to test a ‘new economics of the creative industries’ based on evolutionary economics.

The initial phase of the project saw investment in three areas of infrastructure which formed the platform for ongoing research across the timespan of the Centre: the statistics repository, the categorisation spine and the project wiki.

The project’s statistics repository quickly moved from the exploratory phase (how do we store and manage large datasets with shifting, flexible definition requirements?) to an outcome phase of optimising the disparate datasets in order consistently to produce analytical tables and new insights. The repository (its server, software, and in some cases, data) is now being positioned as eResearch Infrastructure accessible by other QUT researchers and faculties. The repository now manages a considerable wealth of employment-related datasets, including those of the newly-released 2006 census.

To achieve the project objectives, CCI needed to stand outside the ‘definition wars’ which have beset previous projects – endless debate over exactly what constitutes the ‘cultural’, ‘copyright’ or ‘digital content and application’ industries. We therefore developed a consistent approach to managing the measurement and classification of occupations, qualifications and industry activities at the most detailed levels. These categorisation spine codes have proved instrumental in allowing disparate statistics and data to be grouped and analysed consistently and effectively. The strength of the approach was proven in 2007 when it was applied to overseas classification systems.