Interactive Visualization Modeling with CoDe: An Application to Entomological Data

  • Stefania LaudoniaEmail author
  • Marina Margiotta
  • Maurizio Tucci
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 2)


A taxonomy of typical interaction techniques is proposed in [1], where seven categories of information visualizations provided by commercial systems are considered. This framework gives an initial foundation toward a deeper understanding of interaction in Information Visualization, helping discussion and evaluation of interaction techniques. In this chapter we propose a methodology for the specification and design of complex interactive visualizations as an extension of the graphic language CoDe [2]. Based on the seven categories introduced in [1], we add new interaction operators to CoDe, to enable a visualization designer to specify multiple perspectives of a data set, without losing the underlying mental map of the considered information. The new version of CoDe allows to manage some interaction techniques which are difficult to classify and do not quite fit into any of the categories above. Some applications of the proposed methodology to design interactive visualizations of entomological data are provided as a case study.


Information visualization Visual analytics Interaction model CoDe language Entomology 


  1. 1.
    Soo Yi J., ah Kang Y., Stasko J. T. (2007). Toward a deeper understanding of the role of interaction in information visualization. IEEE Transaction on Visualization and Computer Graphics, 13(6), 1224–1231.Google Scholar
  2. 2.
    Ciuccarelli, P., Sessa, M. I., Tucci, M. (2010, October, 8–9) CoDe: a Graphic language for complex system visualization. In Proceedings of ITAIS 2010, Naples (pp. 1–8).Google Scholar
  3. 3.
    Beaudouin-Lafon, M. (2004). Designing interaction, not interfaces. In Proceedings of AVI’04, Gallipoli (LE), Italy (pp. 15–22).Google Scholar
  4. 4.
    Thomas, J. J., & Cook, K. A. (2005). Illuminating the path. Los Alamitos: IEEE.Google Scholar
  5. 5.
    Risi, M., Sessa, M. I., Tortora, G., Tucci, M. (2011, June, 26–29). Visualizing information in data warehouse reports. In Proceedings of SEBD 2011 (pp. 246–257). Italy: Maratea.Google Scholar
  6. 6.
    Russel, S., & Norvig, P. (2009). Artificial intelligence: A modern approach (3rd ed.). Upper Saddle River: Prentice Hall.Google Scholar
  7. 7.
    Bertin, J. (1983). Semiology of graphics: Diagrams, networks, maps. Madison: University of Wisconsin Press.Google Scholar
  8. 8.
    Laudonia, S., & Margiotta, M. (2012). Seasonal phenology of Glycaspis brimblecombei Moore (Hemiptera: Psyllidae), the red gum lerp psyllid, in Italy, (technical report 2012).Google Scholar
  9. 9.
    Viggiani, G. (1997). Lotta biologica ed integrata nelle difesa fitosanitaria (Vol. 1 pp. 42–43). Napoli: Liguori Editore.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefania Laudonia
    • 1
    Email author
  • Marina Margiotta
    • 1
  • Maurizio Tucci
    • 2
  1. 1.Dipartimento di Entomologia e Zoologia Agraria “Filippo Silvestri”University of Naples “Federico II”PorticiItaly
  2. 2.University of SalernoFiscianoItaly

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