Applied Spatial Analysis and Policy

, Volume 5, Issue 1, pp 25–49

The Segmentation of Local Government Areas: Creating a New Geography of Nigeria

  • Adegbola A. Ojo
  • Daniel Vickers
  • Dimitris Ballas
Article

Abstract

Social area classifications group areas on the basis of social or socio-economic similarity into cluster units which define their demographic and social characteristics. The methods used to create these systems combine geographic thought and theory with statistical manipulations of multivariate data. The development and use of geodemographic systems appear to be restricted within developing countries. Some commentators suggest that area classifications may not offer benefits to these countries. This paper argues that the developing world has a lot to benefit from this type of geography. It presents the case of Nigeria where a classification system has been developed for the 774 Local Government Areas (LGA) of the country. Insight is provided into the variables and methodological approach that has been used to create the Nigerian system.

Keywords

Nigeria Area classifications Geodemographics Local Government Areas 

References

  1. Abbas, J., Ojo, A., & Orange, S. (2009). Geodemographics-a tool for health intelligence? Public Health, 123(1), 35–39.CrossRefGoogle Scholar
  2. Berrar, D. P., Dubitzky, W., & Granzow, M. (2003). A practical approach to microarray data analysis. Springer.Google Scholar
  3. Brown, M. C. (1994). Using Gini-style indices to evaluate the spatial patterns of health practitioners: theoretical considerations and an application based on Alberta data. Social Science & Medicine, 38(9), 1243–1256.CrossRefGoogle Scholar
  4. Brown, P. J. B., Hirschfield, A. F. G., & Batey, P. W. J. (2000). Adding value to census data: Public sector applications of super profiles geodemographic typology. Working Paper 56, URPERRL, Department of Civic Design, University of Liverpool.Google Scholar
  5. Burrows, R., & Gane, N. (2006). Geodemographics, software and class. Sociology, 40(5), 793–812.CrossRefGoogle Scholar
  6. Debenham (2002). Understanding geodemographic classification: Creating the building blocks for an extension. Working Paper 02/01, School of Geography, University of Leeds.Google Scholar
  7. Dunteman, G. H. (1989). Principal components analysis. London: Sage Publications, Inc.Google Scholar
  8. Energy Information Administration (2006). Top World Oil Net exporters, 2006 [Online] http://tonto.eia.doe.gov/country/index.cfm. Accessed 8/2/2008.
  9. Everitt, B. S., Landau, S., & Leese, M. (2001). Cluster analysis (4th ed.). London: Arnold.Google Scholar
  10. Fisher, P. F., & Langford, M. (1995). Modeling the errors in areal interpolation between zonal systems by Monte Carlo simulation. Environment and Planning A, 27(2), 211–224.CrossRefGoogle Scholar
  11. Foley, T. (1997). Business minded. New Perspectives, 6, 6.Google Scholar
  12. Gordon, A. (2003). Nigeria’s diverse peoples: A reference source book. Santa Barbara: ABC-CLIO.Google Scholar
  13. Goss, J. (1995). Marketing the new marketing: The strategic discourse of geodemographic information systems. In J. Pickles (Ed.), Ground truth: The social implications of geographic information systems. New York: Guildford Press.Google Scholar
  14. Gregory, I. N. (2000). An evaluation of the accuracy of the areal interpolation of data for the analysis of long-term change in England and Wales [Online] http://www.geocomputation.org/2000/GC045/Gc045.htm. Accessed 5/3/07.
  15. Harris, R., Sleight, P., & Webber, R. (2005). Geodemographics, GIS and neighbourhood targeting. London: Wiley.Google Scholar
  16. Jolliffe, I. T. (2002). Principal components analysis. New York: Springer.Google Scholar
  17. Leventhal, B. (1995). Evaluation of geodemographic classifications. Journal of Targeting, Measurement and Analysis for Marketing, 4(2), 173–183.Google Scholar
  18. Milligan, G. W. (1996). Clustering validation: Results and implications for applied analysis. In P. Arabie, L. J. Hubert, & G. De Soete (Eds.), Clustering and classification. Singapore: World Scientific.Google Scholar
  19. Milligan, G. W., & Stephen, C. H. (2003). Clustering and classification methods. In B. W. Irvin, J. A. Schinka, W. F. Velicer (Eds), Handbook of psychology. New Jersey: John Wiley and Sons Inc.Google Scholar
  20. NBS. (2005). Poverty profile for Nigeria. Nigeria: National Bureau of Statistics.Google Scholar
  21. NBS (2006). Federal Republic of Nigeria 2006 Population Census, Official Gazette (FGP 71/52007/2,500(OL24).Google Scholar
  22. NBS. (2006b). Core welfare indicators questionnaire survey, final statistical report, Federal Republic of Nigeria. Nigeria: National Bureau of Statistics.Google Scholar
  23. NPC. (2004). Meeting everyone’s needs: National economic empowerment development strategy. Nigeria: National Planning Commission.Google Scholar
  24. Ogunbodede, E. F. (2006). Developing geospatial information for poverty reduction: Lessons and challenges from Nigeria’s 2006 Census. GSDI-9 Conference Proceedings, Santiago, Chile.Google Scholar
  25. Okonjo-Iweala, N., & Osafo-Kwaako, P. (2007). Nigeria’s economic reforms: Progress and challenges. Massachusetts: The Brookings Institution.Google Scholar
  26. Olowu, D., & Ayo, S. B. (1985). Local government and community development in Nigeria: Developments since the 1976 Local Government Reform. Community Development Journal, 20(4), 283–292.CrossRefGoogle Scholar
  27. Orford, S., Dorling, D., Mitchell, R., Shaw, M., & Smith, G. D. (2002). Life and death of the people of London: a historical GIS of Charles Booth’s inquiry. Health & Place, 8(1), 25–35.CrossRefGoogle Scholar
  28. Robson, B. T. (1971). Urban analysis: a study of city structure. Cambridge: Cambridge University Press.Google Scholar
  29. Simey, T., & Simey, M. (1960). Charles Booth. Social scientist. London: Oxford University Press.Google Scholar
  30. Singleton, A. D., & Longley, P. (2009). Creating open source geodemographics: refining a national classification of census output areas for applications in higher education. Papers in Regional Science, 88, 643–666.CrossRefGoogle Scholar
  31. Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit Region. Economic Geography, 46(2), 234–240.CrossRefGoogle Scholar
  32. UNESCO (2000). UNESCO/Nigeria Co-operation for Universal Basic Education. United Nations Educational, Scientific and Cultural Organization. [Online] http://unesdoc.unesco.org/images/0014/001485/148544eo.pdf, Accessed 15/08/2007.
  33. United Nations (2007). World population prospects: The 2006 Revision, United Nations Population Division, New York, U.S.A. [Online] http://www.un.org/esa/population/publications/wpp2006/English.pdf, Accessed 13/02/2008.
  34. Urdan, T. C. (2005). Statistics in plain english. New Jersey: Lawrence Erlbaum Associates.Google Scholar
  35. Vickers, D. W. (2006). Multi-level integrated classifications based on the 2001 census. Unpublished PhD thesis. School of Geography, University of Leeds.Google Scholar
  36. Vickers, D., & Rees, P. (2006). Introducing the area classification of output areas. Population Trends, 125, 15–29.Google Scholar
  37. Voas, D., & Williamson, P. (2001). The diversity of diversity: a critique of geodemographic classification. Area, 33(1), 63–76.CrossRefGoogle Scholar
  38. Webber, R. (1977). The national classification of residential neighbourhoods: an introduction to the classification of wards and parishes. Planning Research Applications Group, Centre for Environmental Studies, 23.Google Scholar
  39. World Bank (1996a). Nigeria, poverty in the midst of plenty, the challenge of growth with inclusion: A world bank poverty assessment. [Online] http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/1996/05/31/000009265_3961029235646/Rendered/PDF/multi0page.pdf. Accessed 10/7/2007.
  40. World Bank (1996b). Nigeria: Targeting communities for effective poverty alleviation [Online] http://www.worldbank.org/afr/findings/english/find68.htm. Accessed 02/04/2008.
  41. World Bank (2007a). Independent evaluation group approach paper Nigeria: Country assistance evaluation [Online] http://lnweb18.worldbank.org/oed/oeddoclib.nsf/DocUNIDViewForJavaSearch/3EE7F2E5A37A9BEC85257321007A7697/$file/nigeria_cae_approach_paper.pdf. Accessed 7/05/2008.
  42. World Bank. (2007). World development indicators 2007. Washington, D.C.: World Bank Publications.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Adegbola A. Ojo
    • 1
  • Daniel Vickers
    • 1
  • Dimitris Ballas
    • 1
  1. 1.Department of GeographyUniversity of SheffieldSheffieldUK

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