Geographic Analysis of Cultural Consumption

  • Orian BrookEmail author
  • Paul Boyle
  • Robin Flowerdew
Part of the Understanding Population Trends and Processes book series (UPTA, volume 2)


In this chapter, the authors make use of a large database on ticket sales relating to the performing arts in London to investigate the propensities at which different sociodemographic groups use cultural provision and to develop an ecological model of attendance which includes derived variables relating to the geographical accessibility of purchasing addresses and commuting patterns. They find that the level of higher education qualifications predicts attendance better than other variables, with income and socioeconomic factors relatively insignificant but there is substantial variation in the importance of higher education by venue. Geographical accessibility and commuting patterns are found to be strongly predictive of attendance.


Parsimonious Model Minority Ethnic Group High Education Qualification Priority Group Accessibility Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by the Understanding Population Trends and Processes initiative, funded by the Economic and Social Research Council, and is based on data owned by Audiences London and Arts About Manchester, the collection of which was funded by the Arts Council of England. Professor Richard Webber provided advice on modelling using Mosaic segmentation variables and Dr Paul Norman supplied both advice on population estimates and data for modelling using the Townsend deprivation score. Special Workplace Statistics were supplied by the Centre for Interaction Data and Research, based at the Universities of Leeds and St Andrews. Census output is Crown Copyright and is reproduced with the permission of the Controller of HMSO and the Queen’s Printer for Scotland. Census boundary data, 2001 were supplied by The Office for National Statistics.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.School of Geography and Geosciences, University of St AndrewsSt AndrewsUK
  2. 2.Longitudinal Studies Centre – ScotlandUniversity of St AndrewsScotlandUK

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