Methodology for Clustering Cities Affected by Natural Disasters

  • Fabiana Santos Lima
  • Daniel de Oliveira
  • Mirian Buss Gonçalves
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 275)


This study aims to present a methodology to form clusters by analyzing historical data of disasters in the state of Santa Catarina - Brazil, using the k-means method as a tool for pattern analysis. It can therefore assist in the strategic coordination, in the definition of priorities and in the share of experiences between cities. Therefore, the proposed methodology aims to identify similar regions in order to standardize and suggest a method of prevention and, thus, improve and assist the processes of decision-making regarding the events of Humanitarian Logistics. A computational experiment, applying the proposed methodology, was performed and the obtained results are presented and analyzed at the end.


Humanitarian Logistics Clustering Natural Disasters 


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  1. 1.
    Sapir, G.D.: Disasters in Numbers 2010, Geneva. CRED, Catholic University of Louvain, Brussels, Belgium (2011)Google Scholar
  2. 2.
    CREA, Revista do Conselho Regional de Engenharia, Arquitetura e Agronomia de Santa Catarina. Ano 6(9) maio, Brasil (2011)Google Scholar
  3. 3.
    Fenton, G.: Coordination in the Great Lakes, Forced Migration. Review, 23–24 (September 2003)Google Scholar
  4. 4.
    Rey, F.: The complex nature of actors in humanitarian action and the challenge of coordination. In: Humanitarian Studies Unit (ed.) Reflections on Humanitarian Action: Principles, Ethics and Contradictions. TNI/Pluto Press with Humanitarian Studies Unit and ECHO (European Commission Humanitarian Office), London (2001)Google Scholar
  5. 5.
    Tufinkgi, P.: Logistik im kontext internationaler katastrophenhilfe: Entwicklung eines logistischen referenzmodells für katastrophenfälle. Haupt Verlag, Bern (2006)Google Scholar
  6. 6.
    Schultz, S.F.: Disaster Relief Logistics:Benefits of and Impediments to Horizontal Cooperation between Humanitarian Organizations. Tese. Technischen Universität Berlin (2008)Google Scholar
  7. 7.
    Balcik, B., Beamon, B.M., Krejci, C., Muramatsu, K.M., Ramirez, M.: Coordination in humanitarian relief chains: Practices, challenges and opportunities. International Journal Production Economics. Science Direct 126(1), 22–34 (2010)CrossRefGoogle Scholar
  8. 8.
    Chandes, J., Paché, G.: Investigating humanitarian logistics issues: from operations management to strategic action. Journal of Manufacturing Technology Management 21(3), 320–340 (2011)Google Scholar
  9. 9.
    Kovacs, G., Spens, K.M.: Humanitarian logistics in disaster relief operations. International Journal of Physical Distribution & Logistics Management 37(2), 99–114 (2007)CrossRefGoogle Scholar
  10. 10.
    Kovacs, G., Spens, K.M.: Identifying challenges in humanitarian logistics. International Journal of Physical Distribution & Logistics Management 39(6), 506–528 (2009)CrossRefGoogle Scholar
  11. 11.
    Van Wassenhove, L.V.: Humanitarian Aid Logistics: Supply Chain Managent in High Gear. Journal of the Operational Research Society 57(5), 475–489 (2006)CrossRefMATHGoogle Scholar
  12. 12.
    Tomasini, R., Van Wassenhove, L.V.: Humanitarian logistics. Insead Business Press (2009)Google Scholar
  13. 13.
    Blecken, A.: A Reference Task Model for Supply Chain Processes of Humanitarian Organizations. Doctorate Thesis. Institute of the University of Paderborn (2010)Google Scholar
  14. 14.
    Meirim, H.: Logística humanitária e logística Empresarial, mmrbrasil. Information,
  15. 15.
    Thomas, A., Kopczak, L.: From logistics to supply chain management. The path forward in the humanitarian sector, Fritz Institute, Information,
  16. 16.
    Chu, H.J., Liau, C.J., Lin, C.H., Su, B.S.: Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the Taiwan region. Original Research Article Expert Systems with Applications 39(10), 9451–9457 (2012)CrossRefGoogle Scholar
  17. 17.
    Chang, L.C., Shen, H.Y., Wang, Y.F., Huang, J.Y.: Clustering-based hybrid inundation model for forecasting flood inundation depths. Original Research Article Journal of Hydrology 385(1-4), 257–268 (2010)Google Scholar
  18. 18.
    Wan, S.: Entropy-based particle swarm optimization with clustering analysis on landslide susceptibility mapping. Springer (2012)Google Scholar
  19. 19.
    Acosta, M., Goncalves, M., Vidal, M.E.: CAREY: ClimAtological ContRol of EmergencY Regions. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM-WS 2011. LNCS, vol. 7046, pp. 494–503. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    Jahre, M., Jensen, M.: Coordination in humanitarian logistics through clusters. International Journal of Physical Distribution & Logistics Management 40(8/9), 657–674 (2010)CrossRefGoogle Scholar
  21. 21.
    Dalal, J., Mohapatra, P.K.J., Mitra, G.C.: Locating cyclone shelters: a case. Emerald Article. Disaster Prevention and Management 16(2), 235–244 (2007)CrossRefGoogle Scholar
  22. 22.
    Jahre, M., Navangul, A.K.: Predisting the unpredictable – Demand Forecasting in International Humanitarian Response. In: Proceedings of the 23rd Annual NOFOMA Conference, Harstad, Norway, pp. 265–281 (2011)Google Scholar
  23. 23.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On Clustering Validation Techniques. Journal of Intelligent Information Systems 17(2/3), 107–145 (2001)CrossRefMATHGoogle Scholar
  24. 24.
    Bandyopadhyay, S.: Genetic algorithms for clustering and fuzzy clustering. WIREs Data Mining and Knowledge Discover 1, 524–531 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fabiana Santos Lima
    • 1
  • Daniel de Oliveira
    • 1
  • Mirian Buss Gonçalves
    • 1
  1. 1.Department of Production and Systems EngineeringFederal University of Santa CatarinaFlorianopolisBrazil

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