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Visualization of Zoomable 2D Projections on the Web

  • Michael Maus
  • Tobias Ruppert
  • Arjan KuijperEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10923)

Abstract

The objective of the work is the research and development of a web-based visualization system for the creation and testing of zoomable projection cards. The basic idea is to project a multidimensional data set onto two dimensions using projection methods to represent it on a 2D surface. Based on the Card, Mackinlay, and Shneiderman visualization pipeline, a data processing model has been developed. For data processing various distance metrics, dimension reduction methods, zooming approaches as well as presentation concepts are considered. The peculiarities and considerations of the respective technology are discussed. A zooming approach allows large amounts of data to be displayed on a limited area. In order to better visualize connections within the data, concepts of presentation are discussed. The data points are represented as glyph-based objects or using color maps, various shapes, and sizes. Best practices about colormaps are discussed. In order to display large amounts of data in real time, a separation of the generation and visualization process takes place. During generation, a tabular file and selected configuration execute computationally-intensive transformation processes to create map material. Similar to Google Maps, the generated map material is represented by a visualization. Management concepts for managing various map sets as well as their generation and presentation are presented. A user interface can be used to create and visualize map material. The user uploads a tabular file into the system and chooses between different configuration parameters. Subsequently, this information is used to generate map material. The maps and various interaction options are provided in the visualization interface. Using various application examples, the advantages of this visualization system are presented.

Keywords

Visualization Zooming Projection 

References

  1. 1.
    Bederson, B.B., Hollan, J.D., Perlin, K., Meyer, J.M., Bacon, D., Furnas, G.W.: Pad++: a zoomable graphical sketchpad for exploring alternate interface physics. J. Vis. Lang. Comput. 7, 3–32 (1996)CrossRefGoogle Scholar
  2. 2.
    Boulos, M.N.K.: The use of interactive graphical maps for browsing medical/health internet information resources. Int. J. Health Geogr. 2(1), 1 (2003)CrossRefGoogle Scholar
  3. 3.
    Burkhardt, D., Nazemi, K., Breyer, M., Stab, C., Kuijper, A.: SemaZoom: semantics exploration by using a layer-based focus and context metaphor. In: Kurosu, M. (ed.) HCD 2011. LNCS, vol. 6776, pp. 491–499. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21753-1_55CrossRefGoogle Scholar
  4. 4.
    Card, S.K., Mackinlay, J.D., Shneiderman, B. (eds.): Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  5. 5.
    Gutbell, R., Kuehnel, H., Kuijper, A.: Texturizing and refinement of 3D city models with mobile devices. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2017. LNCS, vol. 10617, pp. 313–324. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70353-4_27CrossRefGoogle Scholar
  6. 6.
    Kuijper, A.: On detecting all saddle points in 2D images. Pattern Recogn. Lett. 25(15), 1665–1672 (2004)CrossRefGoogle Scholar
  7. 7.
    Kuijper, A.: Using catastrophe theory to derive trees from images. J. Math. Imaging Vis. 23(3), 219–238 (2005)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kuijper, A., Florack, L.: The relevance of non-generic events in scale space models. Int. J. Comput. Vis. 57(1), 67–84 (2004)CrossRefGoogle Scholar
  9. 9.
    von Landesberger, T., Fiebig, S., Bremm, S., Kuijper, A., Fellner, D.W.: Interaction taxonomy for tracking of user actions in visual analytics applications. In: Huang, W. (ed.) Handbook of Human Centric Visualization, pp. 653–670. Springer, New York (2014).  https://doi.org/10.1007/978-1-4614-7485-2_26CrossRefGoogle Scholar
  10. 10.
    Lins, L., Klosowski, J.T., Scheidegger, C.: Nanocubes for real-time exploration of spatiotemporal datasets. IEEE Trans. Vis. Comput. Graph. 19(12), 2456–2465 (2013)CrossRefGoogle Scholar
  11. 11.
    Maus, M.: Definition und visualisierung von zoombaren 2D-projektionen im web. Technical report, TU Darmstadt (2016)Google Scholar
  12. 12.
    Nazemi, K., Breyer, M., Forster, J., Burkhardt, D., Kuijper, A.: Interacting with semantics: a user-centered visualization adaptation based on semantics data. In: Human Interface and the Management of Information. Interacting with Information - Symposium on Human Interface 2011, Held as Part of HCI International 2011, Orlando, FL, USA, 9–14 July 2011, Proceedings, Part I, pp. 239–248 (2011)Google Scholar
  13. 13.
    Pekalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition: Foundations and Applications. Machine Perception and Artificial Intelligence. World Scientific Publishing Co., Inc., River Edge (2005)CrossRefGoogle Scholar
  14. 14.
    Stahnke, J., Dörk, M., Müller, B., Thom, A.: Probing projections: interaction techniques for interpreting arrangements and errors of dimensionality reductions. IEEE Trans. Vis. Comput. Graph. 22(1), 629–638 (2016)CrossRefGoogle Scholar
  15. 15.
    Zöllner, M., Jetter, H.C., Reiterer, H.: ZOIL: a design paradigm and software framework for Post-WIMP distributed user interfaces. In: Gallud, J., Tesoriero, R., Penichet, V. (eds.) Distributed User Interfaces, pp. 87–94. Springer, London (2011).  https://doi.org/10.1007/978-1-4471-2271-5_10CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Technische Universität DarmstadtDarmstadtGermany
  2. 2.Fraunhofer IGDDarmstadtGermany

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