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.
Chapter PDF
References
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.
Cao L. et al (2007), DDDM2007: Domain Driven Data Mining, SIGKDD Explorations Volume 9, Issue 2, pp 84.
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.
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.
IBGE Brazilian Census 2000.(2005) [On Line] www.ibge.br.
INPE, National Spatial Research Institute. (2005) CBERS. [On Line] www.cbers.inpe.br.
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.
Jim, C.Y. (1989) Tree canopy cover, land use and planning implications in urban Hong Kong. Geoforum, 20(1):57–68.
Kimball, R, (1996). The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses (John Wiley & Sons Inc) 416 pp.
Liu, S. E Zhu, X. (2004) An Integrated GIS approach to accessibility analysis. Transactions in GIS, 8 (1): 45–62, 2004.
Muller, R. J. (1999) “ Database design for smarties: using UML for data modeling ” , San Francisco: Morgan Kaufmann.
Mumbower, L.; Donoghue, J. (1967) “ Urban poverty study. Photogrammetric engineering ” , 33(6):610–618.
Piattini, M. et al. (2001) “ Information and Database Quality ” , Kluwer Academic Publishers.
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.
Strong, D. M. et al. (1997) “ Data Quality in Context ” , Communications of the ACM. New York, vol.40 no 5, p. 103–110, May.
Witten, I. H. & Frank, E. (2005) Data Mining: Practical machine learning tools and techniques. 2nd Edition, Morgan Kaufmann, 560 pp.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Neto, E.R., Hamburger, D.S. (2009). Census Data Mining for Land Use Classification. In: Cao, L., Yu, P.S., Zhang, C., Zhang, H. (eds) Data Mining for Business Applications. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79420-4_17
Download citation
DOI: https://doi.org/10.1007/978-0-387-79420-4_17
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-79419-8
Online ISBN: 978-0-387-79420-4
eBook Packages: Computer ScienceComputer Science (R0)