Advertisement

Spatial Data Mining for Clustering: An Application to the Florentine Metropolitan Area Using RedCap

  • Federico Benassi
  • Chiara Bocci
  • Alessandra Petrucci
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The paper presents an original application of the recently proposed RedCap method of spatial clustering and regionalization on the Florentine Metropolitan Area (FMA). Demographic indicators are used as the input of a spatial clustering and regionalization model in order to classify the FMA’s municipalities into a number of demographically homogeneous as well as spatially contiguous zones. In the context of a gradual decentralization of governance activities we believe the FMA is a representative case of study and that the individuation of new spatial areas built considering both the demographic characteristics of the resident population and the spatial dimension of the territory where this population insists could become a useful tool for local governance.

Keywords

Spatial Cluster Demographic Structure Destination Area Young Couple Spatial Contiguity 
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.

References

  1. Angayarkkani, K., & Radhakrishnan, N. (2009). Efficient forest fire detection system: A spatial data mining and image processing based approach. International Journal of Computer Science and Network Security,9(3), 100–107.Google Scholar
  2. Behnisch, M., & Ultsch, A. (2010). Are there cluster of communities with the same dynamic behavior? In Classification as a tool for research (Part. 3, pp. 445–453). Berlin: SpringerGoogle Scholar
  3. Benassi, F., & Porciani, L. (2010). The dual demographic profile of migration in Tuscany. In T. Salzmann, B. Edmonston, & J. Raymer (Eds.), Demographic aspects of migration (pp. 209–226). Berlin: VS-VERLAG Springer.Google Scholar
  4. Benassi, F., Bottai, M., & Giuliani, G. (2009). Migrazioni e processi di urbanizzazione in Italia. Spunti interpretativi in unottica biografica. In M. J. Macchi (Ed.), Geografie del popolamento: Metodi, casi e teorie (pp. 71–78). Siena: Edizioni dell’Università di Siena.Google Scholar
  5. Guo, D. (2008). Regionalization with dynamically constrained agglomerative clustering and partitioning. International Journal of Geographical Information Sciences,22(7), 801–823.Google Scholar
  6. Guo, D., Gahegan, M., MacEachren, A. M., & Zhou, B. (2005). Multivariate analysis and geovisualization with an integrated geographic knowledge discovery approach. Cartography and Geographic Information Science,32(2), 113–132.Google Scholar
  7. Jin, H., & Guo, D. (2009). Understanding climate change patterns with multivariate geovisualization. In Proceedings of the International Conference on Data Mining Workshops (pp. 217–222). Los Alamitos: IEEE. doi: 10.1109/ICDMW.2009.109.Google Scholar
  8. Koperski, K., Adhikany, J., & Han, J. (1996). Knowledge discovery in spatial database: Progress and challenges. In Proceedings of the Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal. pp. 55–70.Google Scholar
  9. Petrucci, A., Salvati, N., Salvini, S., & Vignoli D. (2008). Invecchiamento e mobilità nell’area metropolitana fiorentina. Rivista di Economia e Statistica del Territorio,2, 81–103.Google Scholar
  10. Roddick, J. F., & Spiliopoulou, M. (1999). A bibliography of temporal, spatio and spatio-temporal data mining research. SIGKDD Explorations,1(1), 34–38.Google Scholar
  11. Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography,46(2), 234–240.Google Scholar
  12. Van den Berg, L., Drewett, R., Klaassen, L. H., Rossi, A., & Vijverberg, C. H. T. (1982). Urban Europe: A study of growth and decline. Oxford: Pergamon.Google Scholar
  13. Vignoli, D., Dugheri, G., Ferro, I., Salvini, S., & Secondi, L. (2007). L’area fiorentina: quanti siamo e quanti saremo. Serie La statistica per la città, Ufficio Statistica del Comune di Firenze.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Federico Benassi
    • 1
  • Chiara Bocci
    • 1
  • Alessandra Petrucci
    • 1
  1. 1.Department of Statistics “G. Parenti”University of FlorenceFlorenceItaly

Personalised recommendations