From Surface to Core: A Multi-Layer Approach for the Real Estate Market Analysis of a Central Area in Catania

  • Laura GabrielliEmail author
  • Salvatore Giuffrida
  • Maria Rosa Trovato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9157)


The proposed study deals with the analysis of the real estate market in the quarter of San Cristoforo in Catania, trying to integrate different approaches to define its possible articulation in submarkets. The first one is a phenomenal type of approach that intends to represent some of the most manifest characteristics, and provides an initial hypothesis of classification of the cases (a census has been taken of) and delimitation of the segments, taking into account the ranges of prices registered inside the different classes of the characteristics. The second consists of an in-depth clustering analysis basing on three different hypotheses of three, four and five clusters respectively. The third one is a DRSA application, which is meant to extract from the studied sample a set of rules for the possible definition of a segment representing the general market rules. Given the complexity of the studied context, the results allow different interpretations and considerations of method.


Real estate market Complex urban context Cluster analysis Dominance-based rough set approach 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baarsch, J., Celebi, M.E.: Investigation of internal validity measures for K-means clustering. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2012, Hong Kong, Vol. I, March 14 – 16, 2012Google Scholar
  2. 2.
    Ball, G., Hall, D.: ISODATA, a novel method of data analysis and pattern classification. Stanford Research Institute, Menlo Park (1965)Google Scholar
  3. 3.
    Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3, 1–27 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Dato, G.: La città di Catania. Forma e struttura, pp. 1693–1833. Officina Edizioni, RomaGoogle Scholar
  5. 5.
    Forte, C.: Elementi di estimo urbano. Etas Kompass, Milano (1968)Google Scholar
  6. 6.
    Greco, S., Matarazzo, B., Słowiński, R.: Rough approximation of a preference relation by dominance relations. European J. Operational Research 117, 63–83 (1999)CrossRefzbMATHGoogle Scholar
  7. 7.
    Greco, S., Matarazzo, B., Słowiński, R.: Rough sets theory for multicriteria decision analysis. European J. of Operational Research 129, 1–47 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Greco, S., Matarazzo, B., Słowiński, R.: Dominance-based rough set approach to knowledge discovery – (I) general perspective, (II) extensions and applications. In: Zhong, N., Liu, J. (eds.) Intelligent Technologies for Information Analysis, pp. 513–612. Springer, Berlin (2004)CrossRefGoogle Scholar
  9. 9.
    Greco, S., Matarazzo, B., Słowiński, R.: Decision rule approach. In: Figueira, J., Greco, S., Ehrgott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 507–563. Springer, Berlin (2005)Google Scholar
  10. 10.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On Clustering Validation Techniques. Journal of Intelligent Information Systems 17(2/3), 107–145 (2001)CrossRefzbMATHGoogle Scholar
  11. 11.
    Hartigan, J.: Clustering algorithms. John Wiley & Sons, Inc., New York (1975)zbMATHGoogle Scholar
  12. 12.
    Jardine, N., Sibson, R.: The construction of hierarchic and non – hierarchic classifications. The Computer Journal 1, 177–184 (1968)CrossRefzbMATHGoogle Scholar
  13. 13.
    Krzanowski, W., Lai, Y.: A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics 44(1), 23–34 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Trovato, M.R., Giuffrida, S.: A DSS to assess and manage the urban performances in the regeneration plan: the case study of pachino. In: Murgante, B., et al. (eds.) ICCSA 2014, Part III. LNCS, vol. 8581, pp. 224–239. Springer, Heidelberg (2014)Google Scholar
  15. 15.
    Trovato, M.: A fuzzy measure of the ability of a real estate capital to increase in value. The real estate decision problem for ortigia, in appraisals. In: Evolving Proceedings in Global Change, vol. 2, pp. 697–720. Firenze University Press, Firenze (2012)Google Scholar
  16. 16.
    Trovato, M.R.: The real estate decision problem. a model to support the real estate market analysis. In: Rèsumés/Abstracts, 78 Meeting of the European Working Group “Multiple Criteria Decision Aiding” Catania 2013, pp. 65–66. University of Catania (2013)Google Scholar
  17. 17.
    Xu, L.: Bayesian ying-yang machine, clustering and number of clusters. Pattern Recogn. Lett. 18, 1167–1178 (1997)CrossRefGoogle Scholar
  18. 18.
    Zhao, Q., Xu, M., Fränti, P.: Sum-of-square based cluster validity index and significance analysis. In: Proc. of the 17th Int. Conf. on Adaptive and Natural Computing Algorithms, pp. 313–322 (2009)Google Scholar
  19. 19.
    Zhao, Q., Fränti, P.: WB-index: A sum-of-squares based index for cluster validity. Data & Knowledge Engineering 92, 77–89 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Laura Gabrielli
    • 1
    Email author
  • Salvatore Giuffrida
    • 2
  • Maria Rosa Trovato
    • 2
  1. 1.Department of ArchitectureUniversity of FerraraFerraraItaly
  2. 2.Department of Civil Engineering and ArchitectureUniversity of CataniaCataniaItaly

Personalised recommendations