Census Data Mining for Land Use Classification

  • E. Roma Neto
  • D. S. Hamburger

This chapter presents spatial data mining techniques applied to support land use mapping. The area of study is in São Paulo municipality. The methodology is presented in three items: extraction, transformation and first analysis; knowledge discovering and supporting rules evaluation; image classification support. The combined inferences resulted in a good improvement in the digital image classification with the contribution of Census data.


Census Data Digital Image Processing Data Warehouse Urban Fringe Data Mining Process 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anderson, J.R.; Hardy, E.E.; Roach, J.T. and Witner, R.E. (1976) “ Sistemas de classifica ç ão do uso do solo para utiliza ç ão com dados de sensoriamento remoto ” , Trad. H.Strang, Rio de Janeiro, IBGE.Google Scholar
  2. 2.
    Cao L. et al (2007), DDDM2007: Domain Driven Data Mining, SIGKDD Explorations Volume 9, Issue 2, pp 84.CrossRefGoogle Scholar
  3. 3.
    Forster, B.C. (1984) Combining ancillary and spectral data for urban applications, International archives photogrammetry and remote sensing. V.XXV part A7, Commission 7, INTERNATIONAL SYMPOSIUM ARCHIVES PHOTOGRAMMETRY AND REMOTE SENSING, XVth Congress, Rio de Janeiro 1984. p.207–216.Google Scholar
  4. 4.
    Forster, B.C. (1985) An examination of some problems and solutions in monitoring urban areas from satellite platforms, International journal of remote sensing, 6(1): 139–151.CrossRefGoogle Scholar
  5. 5.
    IBGE Brazilian Census 2000.(2005) [On Line] Scholar
  6. 6.
    INPE, National Spatial Research Institute. (2005) CBERS. [On Line] Scholar
  7. 7.
    Jensen, J.R. (1983) “ Urban/suburban land use analysis. In: Manual of remote sensing ” 2ed. Falls Church, American Society of Photogrammetry. v.2, chapter.30, p.1571–1666.Google Scholar
  8. 8.
    Jim, C.Y. (1989) Tree canopy cover, land use and planning implications in urban Hong Kong. Geoforum, 20(1):57–68.CrossRefGoogle Scholar
  9. 9.
    Kimball, R, (1996). The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses (John Wiley & Sons Inc) 416 pp.Google Scholar
  10. 10.
    Liu, S. E Zhu, X. (2004) An Integrated GIS approach to accessibility analysis. Transactions in GIS, 8 (1): 45–62, 2004.CrossRefGoogle Scholar
  11. 11.
    Muller, R. J. (1999) “ Database design for smarties: using UML for data modeling ” , San Francisco: Morgan Kaufmann.Google Scholar
  12. 12.
    Mumbower, L.; Donoghue, J. (1967) “ Urban poverty study. Photogrammetric engineering ” , 33(6):610–618.Google Scholar
  13. 13.
    Piattini, M. et al. (2001) “ Information and Database Quality ” , Kluwer Academic Publishers.Google Scholar
  14. 14.
    Roma Neto, E. ; Hamburger, D. S. Data warehouse and spatial data mining as a support to urban land use mapping using digital image classification - A study on Sao Paulo Metropolitan area with CBERS - 2 Data. In: 25th Urban Data Management Symposium, Aalborg, 2006.Google Scholar
  15. 15.
    Strong, D. M. et al. (1997) “ Data Quality in Context ” , Communications of the ACM. New York, vol.40 no 5, p. 103–110, May.Google Scholar
  16. 16.
    Witten, I. H. & Frank, E. (2005) Data Mining: Practical machine learning tools and techniques. 2nd Edition, Morgan Kaufmann, 560 pp.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • E. Roma Neto
    • 1
  • D. S. Hamburger
    • 1
  1. 1.Av. Eng. Euséio StevauxSao PauloBrazil

Personalised recommendations