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Using GIS to Derive Region-Wide Patterns of Quality of Urban Life Dimensions: Illustrated with Data from the Brisbane-SEQ Region

  • Prem ChhetriEmail author
  • Robert Stimson
  • John Western
Chapter
Part of the Social Indicators Research Series book series (SINS, volume 45)

Abstract

The chapter demonstrates how statistical analysis and GIS tools are used to derive spatially generalized patterns of subjective assessments of aspects of QOL dimensions. Data collected in the 2003 Brisbane-Southeast Queensland (SEQ) QOL survey are used in the analysis. One approach demonstrates the use of an “ordered weighted average” nonlinear aggregation technique to derive generalized patterns of subjective assessments of QOUL dimensions across subregions. Another approach demonstrates how patterns of underlying dimensions of attractiveness of neighborhood attributes affecting peoples’ choices in where to live may be simulated and mapped using the “neighborhood operation” function in GIS.

Keywords

Geographic Information System Ordered Weighted Average Statistical Local Area Residential Location Choice Neighborhood Operation 
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.

Notes

Acknowledgement

This chapter is based on research funded by the Australian Research Council Discovery, project # DP0209146.

References

  1. Arrowsmith, C., & Inbakaran, R. (2002). Estimating environmental resiliency for the Grampians National Park. Tourism Management, 23(3), 295–309.CrossRefGoogle Scholar
  2. Bishop, I. D., & Hulse, D. W. (1994). Prediction of scenic beauty using mapped data and geographic information systems. Landscape and Urban Planning, 30, 59–70.CrossRefGoogle Scholar
  3. Burley, J. B., & Brown, T. J. (1995). Constructing interpretable environments from multidimensional data: GIS suitability overlays and principal component analysis. Journal of Environmental Planning and Management, 38(4), 537–550.CrossRefGoogle Scholar
  4. Cheshire, P., & Sheppard, S. (1995). Evaluating the impact of neighbourhood effects on house prices and land rents: results from an extended model. Economica, 62, 247–267.CrossRefGoogle Scholar
  5. Chhetri, P., & Arrowsmith, C. (2002). Developing a spatial model of hiking experiences in natural landscapes. Cartography, 13(2), 87–102.Google Scholar
  6. Chhetri, P., Stimson, R. J., Western, J., & Shyy, T-K. (2005) What matters in a neighbourhood?. In Proceedings of spatial sciences conference 2005: Spatial intelligence, innovation and praxis. Melbourne: Spatial Sciences Institute.Google Scholar
  7. Chhetri, P., Stimson, R. J., & Western, J. (2006). Modelling the factors of neighbourhood attractiveness reflected in residential location decision choices. Studies in Regional Science, 36(2), 35–45.CrossRefGoogle Scholar
  8. Chhetri, P., Stimson, R. J., Akbar, D., & Western, J. (2007). Developing perceived quality of life indices: an application of ordered weighted average operators. Studies in Regional Science, 37, 553–572.CrossRefGoogle Scholar
  9. Cutter, S. L. (1985). Rating places: A geographer’s view on quality of life. Washington, DC: Association of American Geographers, Resource Publication in Geography.Google Scholar
  10. Dökmeci, V., & Berköz, L. (2000). Residential-location preferences according to demographic characteristics in Istanbul. Landscape and Urban Planning, 48, 45–55.CrossRefGoogle Scholar
  11. Filev, D., & Yager, R. R. (1995). Analytical properties of maximum entropy OWA operators. Information Sciences, 85, 11–27.CrossRefGoogle Scholar
  12. Filev, D., & Yager, R. R. (1998). On the issue of obtaining OWA operator weights. Fuzzy Sets and Systems, 94, 157–169.CrossRefGoogle Scholar
  13. Golledge, R. G., & Stimson, R. J. (1997). Spatial behaviour a geographer perspective. New York: The Guilford Press.Google Scholar
  14. Hair, J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate data analysis: With readings. Englewood Cliffs: Prentice-Hall.Google Scholar
  15. Johnston, D. F. (1988). Toward a comprehensive ‘quality of life’ index. Social Indicator Research, 20, 190–214.Google Scholar
  16. Kliskey, A. D. (2000). Recreation terrain suitability mapping: a spatially explicit methodology for determining recreation potential for resource use assessment. Landscape and Urban Planning, 52, 30–43.CrossRefGoogle Scholar
  17. Lee, J., & Wong, D. W. S. (2001). Statistical analysis with ArcView GIS. New York: Wiley.Google Scholar
  18. Levinsohn, A., Langford, G., Rayner, M., Rintoul, J. & Eccles, R. (1987). A micro-computer based GIS for assessing recreation suitability. In Proceedings of GIS 87 (pp. 739–747). Falls Church: American Society for Photogrammetry and Remote Sensing.Google Scholar
  19. Linton, D. L. (1968). The assessment of scenery as a natural resource. Scottish Geographical Magazine, 84, 219–238.CrossRefGoogle Scholar
  20. Maher, C. A., & Saunders, E. (1994). Changing patterns of relocation within a Metropolitan Area: Intraurban mobility in Melbourne 1971–9. Paper presented to Workshop on Mobility, Melbourne.Google Scholar
  21. Marans, R. W. (2003). Understanding environmental quality through quality of life studies: The 2001 DAS and its use of subjective and objective indicators. Landscape and Urban Planning, 65(1), 73–83.CrossRefGoogle Scholar
  22. Mendes, J. F. G., & Motizuki, W. S. (2001). Urban quality of life evaluation scenarios: The case of Sao Carlos in Brazil. CTBUH Review, 1(2), 1–11.Google Scholar
  23. Mingche, M. L., & Brown, J. H. (1980). Micro-neighbourhood externalities and hedonic housing price. Land Economics, 56, 125–141.CrossRefGoogle Scholar
  24. Nie, N. H., Hull, C. G., Jenkins, J. G., Steinbrenner, K., & Bent, D. H. (1975). Statistical package for the social sciences (2nd ed.). New York: McGraw-Hill.Google Scholar
  25. Nijkamp, P., Rietveld, P., & Voogd, H. (1990). Multicriteria evaluation in physical planning. Amsterdam: Elsevier Science Publishers.Google Scholar
  26. Orford, S. (1997). Valuing the built environment: A GIS approach to the Hedonic Modelling of housing markets. Unpublished Ph.D. thesis, University of Bristol, Bristol.Google Scholar
  27. Pinch, S. (1985). Cities and services: The geography of collective consumption. London: Routledge and Kegan Paul.Google Scholar
  28. Rogerson, R. J., Findlay, A. M., & Morris, A. S. (1989). Indicators of quality of life: Some methodological issues. Environment and Planning A, 21(12), 1655–1666.CrossRefGoogle Scholar
  29. Simpson, E. H. (1949). Measurement of diversity. Nature, 163, 688.CrossRefGoogle Scholar
  30. Smith, P. N. (2001). Numeric ordered weighted averaging operators: Possibilities for environmental project evaluation. Journal of Environmental System; 2000–2001, 28(3), 175–191.Google Scholar
  31. Stimson, R.J, (1978). Social space, preference space and residential location behaviour: A social geography of Adelaide. Unpublished Doctoral thesis, Department of Geography, Flinders University, Adelaide.Google Scholar
  32. Stimson, R. J., Chhetri, P., & Western, J. (2006, February). Deriving metro-wide subjective assessments of neighbourhood attraction from sample survey data: An application in the Brisbane-South East Queensland region, Australia. In Western regional science association, 45th Annual Conference, Santa Fe, February.Google Scholar
  33. Tabachnick, B., & Fidell, L. (1996). Using multivariate statistics. New York: Harper Collins.Google Scholar
  34. Talen, E., & Anselin, L. (1998). Assessing spatial equity: An evaluation of measures of accessibility to public playgrounds. Environment and Planning A, 30, 595–613.CrossRefGoogle Scholar
  35. Vogt, A. C., & Marans, R. W. (2004). Natural resources and open space in the residential decision process: A study of recent movers to fringe counties in southeast Michigan. Landscape and Urban Planning, 69, 255–269.CrossRefGoogle Scholar
  36. Western, J., & Larnach, A. (1998). The social and spatial structure of South-East Queensland. Australasian Journal of Regional Studies, 4(2), 215–237.Google Scholar
  37. Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems, Man and Cybernetics, 18(1), 183–190.CrossRefGoogle Scholar
  38. Yager, R. R. (2004). OWA aggregation over a continuous interval argument with applications to decision making. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetic, 34(5), 1951–1963.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.School of Business IT and LogisticsRMIT UniversityMelbourneAustralia
  2. 2.Australian Urban Research Infrastructure Network (AURIN), Faculty of Architecture, Building and PlanningUniversity of MelbourneMelbourneAustralia
  3. 3.School of Social ScienceThe University of QueenslandBrisbaneAustralia

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