Skip to main content

Advertisement

Log in

Geodemographics and spatial interaction: an integrated model for higher education

  • Original Article
  • Published:
Journal of Geographical Systems Aims and scope Submit manuscript

Abstract

Spatial interaction modelling and geodemographic analysis have each developed as quite separate research traditions. In this paper, we present an integrated model that harnesses the power of spatial interaction modelling to behavioural insights derived from a geodemographic classification. This approach is applied to the modelling of participation in higher education (HE). A novel feature of the paper is the integration of national schools, colleges and HE data; a national model is then calibrated and tested against actual recorded flows of students into HE. The model is implemented within a Java framework and is presented as a first step towards providing a quantitative tool that can be used by HE stakeholders to explore policies relating to such topics as widening access to under-represented groups.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Ashby DI, Longley PA (2005) Geocomputation, geodemographics and resource allocation for local policing. Trans GIS 9(1):53–72

    Article  Google Scholar 

  • Aveyard P, Manaseki S, Chambers J (2002) The relationship between mean birth weight and poverty using the townsend deprivation score and the super profile classification system. Public Health 116(6):308–314

    Article  Google Scholar 

  • Batey P, Brown PJB, Corver M (1999) Participation in higher education: a geodemographic perspective on the potential for further expansion in student numbers. J Geogr Syst 1(3):277–303

    Article  Google Scholar 

  • Birkin M (1995) Customer targeting, geodemographic and lifestyles approaches. In: Longley PA, Clarke GP (eds) GIS for business and service planning. Geoinformation, Cambridge, pp 104–149

    Google Scholar 

  • Birkin M, Clarke G (1998) GIS, geodemographics, and spatial modeling in the UK financial service industry. J Hous Res 9(1):87–111

    Google Scholar 

  • Birkin M, Clarke G, Clarke M (2002) Retail geography and intelligent network planning. Wiley, Chichester

    Google Scholar 

  • Birkin M, Clarke G, Clarke M, Culf R (2003) Using spatial models to solve difficult retail location problems. In: Stillwell J, Clarke G (eds) Applied GIS and spatial analysis. Wiley, Chichester, pp 35–54

    Google Scholar 

  • Black JA, Salter RJ (1975) A statistical evaluation of the accuracy of a family of gravity models. Proc Inst Civ Eng 59:1–26

    Article  Google Scholar 

  • Bowden R (2000) Fantasy higher education: university and college league tables. Qual High Educ 6(1):41–60

    Article  Google Scholar 

  • Bureau of Public Roads (1965) Calibrating and testing a gravity model for any size urban area. US Dept of Commerce—Bureau of Public Roads, Washington DC

    Google Scholar 

  • Comptroller Auditor General (2008) Widening participation in higher education. National Audit Office, London

    Google Scholar 

  • Erlander S, Stewart NF (1990) The gravity model in transportation analysis: theory and extensions. VSP BV, The Netherlands

    Google Scholar 

  • Feng Z, Flowerdew R (1998) Fuzzy geodemographics: a contribution from fuzzy clustering methods. In: Carver S (ed) Innovations in GIS 5. Taylor & Francis, Oxford, pp 119–127

    Google Scholar 

  • Fotheringham AS (1983) A new set of spatial interaction models: the theory of competing destinations. Environ Plan A 15(1):15–36

    Article  Google Scholar 

  • Fotheringham AS, Brunsdon C, Charlton M (2000) Quantitative geography: perspectives on spatial data analysis. Sage, London

    Google Scholar 

  • Han Q, Timmermans H (2006) Towards models of strategic spatial choice behaviour: theory and application issues. GeoJournal 67(3):195–206

    Article  Google Scholar 

  • Harris R, Sleight P, Webber R (2005) Geodemographics, GIS and neighbourhood targeting. Wiley, Chichester

    Google Scholar 

  • Harris R, Johnston R, Burgess S (2007) Neighborhoods, ethnicity and school choice: developing a statistical framework for geodemographic analysis. Popul Res Policy Rev 26(5–6):553–579

    Article  Google Scholar 

  • Longley PA (2005) Geographical information systems: a renaissance of geodemographics for public service delivery. Prog Hum Geogr 29(1):57–63

    Article  Google Scholar 

  • Singleton AD (2010) Educational opportunity: the geography of access to higher education. Ashgate, Farnham

    Google Scholar 

  • Singleton AD, Longley PA (2009) Creating open source geodemographics—refining a national classification of census output areas for applications in higher education. Prog Hum Geogr 88(3):643–666

    Google Scholar 

  • Sleight P (1997) Targeting customers: how to use geodemographic and lifestyle data in your business. NTC Publications, Henley-on-Thames

    Google Scholar 

  • Stillwell JCH (1978) Interzonal migration: some historical tests of spatial interaction models. Environ Plan A 10:1187–1200

    Article  Google Scholar 

  • Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46(2):234–240

    Article  Google Scholar 

  • Tonks D, Farr M (2003) Widening access and participation in UK higher education. Int J Educ Manage 17(1):26–36

    Article  Google Scholar 

  • Turner D (2005) Benchmarking in universities: league tables revisited. Oxford Rev Educ 31(3):353–371

    Article  Google Scholar 

  • Twigg L, Moon G, Jones K (2000) Predicting small-area health-related behaviour: a comparison of smoking and drinking indicators. Soc Sci Med 50(7–8):1109–1120

    Article  Google Scholar 

  • Vickers D, Rees P (2007) Creating the UK national statistics 2001 output area classification. J R Stat Soc Ser A Stat Soc 170(2):379–403

    Article  Google Scholar 

  • Voas D, Williamson P (2001) The diversity of diversity: a critique of geodemographic classification. Area 33(1):63–76

    Article  Google Scholar 

  • Wilson AG (1970) Entropy in urban and regional modelling. Pion, London

    Google Scholar 

  • Wilson AG (1998) Land-use/transport interaction models: past and future. J Transp Econ Policy 32(1):3–26

    Google Scholar 

  • Wilson AG (2000) The widening access debate: student flows to universities and associated performance indicators. Environ Plan A 32(11):2019–2031

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. D. Singleton.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Singleton, A.D., Wilson, A.G. & O’Brien, O. Geodemographics and spatial interaction: an integrated model for higher education. J Geogr Syst 14, 223–241 (2012). https://doi.org/10.1007/s10109-010-0141-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10109-010-0141-5

Keywords

Navigation