iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/978-3-642-41939-3_9
Visual Statistics Cockpits for Information Gathering in the Policy-Making Process | SpringerLink
Skip to main content

Visual Statistics Cockpits for Information Gathering in the Policy-Making Process

  • Conference paper
Advances in Visual Computing (ISVC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8034))

Included in the following conference series:

Abstract

A major step in ICT-driven policy making is information gathering. During this phase, analysts and experts have to deal with a high number of statistical data which they use as a basis to identify problems and find appropriate solutions. This paper introduces a statistical data model to support these analysts and experts. It allows for handling the complexity (i.e. the dimensions) of the data for the visualizations. In particular, it helps to use the same data for two-dimensional, but also multi-dimensional statistics visualizations. Based on this statistic data model we introduce an interactive approach of visual statistics cockpits. This results in highly interactive statistics visualization cockpits that enable both analysts and experts to improve problem assessment and solution finding.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Hao, M., Dayal, U., Keim, D., Schreck, T.: A Visual Analysis of Multi-Attribute Data Using Pixel Matrix Displays. In: Proc. VDA 2007 (2007)

    Google Scholar 

  2. Yuan, X., Guo, P., Xiao, H., Zhou, H., Qu, H.: Scattering Points in Parallel Coordinates. IEEE Transactions on Visualization and Computer Graphics 15(6), 1001–1008 (2009)

    Article  Google Scholar 

  3. Heinrich, J., Weiskopf, D.: Continuous Parallel Coordinates. IEEE Transactions on Visualization and Computer Graphics 15(6), 1531–1538 (2009)

    Article  Google Scholar 

  4. Ward, M.O., Grinstein, G., Keim, D.: Interactive Data Visualizations: Foundations, Techniques, and Applications. Taylor & Francis Ltd. (2010)

    Google Scholar 

  5. Viau, C., McGuffin, M.J., Chiricota, Y., Jurisica, I.: The FlowVizMenu and Parallel Scatterplot Matrix: Hybrid Multidimensional Visualizations for Network Exploration. IEEE Transactions on Visualization and Computer Graphics 16(6), 1100–1108 (2010)

    Article  Google Scholar 

  6. Sharko, J., Grinstein, G., Marx, K.A.: Vectorized Radviz and Its Application to Multiple Cluster Datasets. IEEE Transactions on Visualization and Computer Graphics 14(6), 1077–1427 (2008)

    Article  Google Scholar 

  7. Draper, G., Livnat, Y., Riesenfeld, R.F.: A Survey of Radial Methods for Information Visualization. IEEE Transactions on Visualization and Computer Graphics 15(5), 759–776 (2009)

    Article  Google Scholar 

  8. Wilkinson, L., Friendly, M.: The History of the Cluster Heat Map. The American Statistician 63(2), 179–184 (2009)

    Article  MathSciNet  Google Scholar 

  9. Rao, R., Card, S.: The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems: Celebrating Interdependence, CHI 1994, pp. 318–322 (1994)

    Google Scholar 

  10. Keim, D.A., Kriegel, H.-P., Ankerst, M.: Recursive pattern: a technique for visualizing very large amounts of data. In: Proceedings of the 6th Conference on Visualization 1995, pp. 279–286 (1995)

    Google Scholar 

  11. Havre, S., Hetzler, E., Whitney, P., Nowell, L.: ThemeRiver: visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics 8(1), 9–20 (2002)

    Article  Google Scholar 

  12. Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings IEEE Symposium on Visual Languages, pp. 336–343 (1996)

    Google Scholar 

  13. Jern, M.: Collaborative web-enabled geoanalytics applied to OECD regional data. In: Luo, Y. (ed.) CDVE 2009. LNCS, vol. 5738, pp. 32–43. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Cyganiak, R., Field, S., Gregory, A., Halb, W., Tennison, J.: Semantic Statistics: Bringing Together SDMX and SCOVO. In: Proceedings of LDOW, Raleigh, North Carolina, USA (2010)

    Google Scholar 

  15. Few, S.: Information Dashboard Design. Overview article about Dashboards (2012), http://blogs.ischool.berkeley.edu/i247s12/files/2012/01/Dashboard-Design-Overview-Presentation.pdf

  16. Duval, E.: Attention please! learning analytics for visualization and recommendation. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, LAK 2011, pp. 9–17. ACM, New York (2011)

    Google Scholar 

  17. Kohlhammer, J., Nazemi, K., Ruppert, T., Burkhardt, D.: Toward Visualization in Policy Modeling. IEEE Computer Graphics and Applications 32(5), 84–89 (2012)

    Article  Google Scholar 

  18. Burkhardt, D., Nazemi, K., Sonntagbauer, P., Sonntagbauer, S., Kohlhammer, J.: Interactive Visualizations in the Process of Policy Modeling. In: Proceedings of IFIP eGov 2013. GI-LNI (2013)

    Google Scholar 

  19. Jern, M.: (OECD), What does OECD eXplorer enable you to do? An introduction to its main features. In: Handbook of the OECD eXplorer (2009), http://www.oecd.org/gov/43142629.pdf

  20. Burkhardt, D., Ruppert, T., Nazemi, K.: Towards process-oriented Information Visualization for supporting users. In: Proceedings of 15th International Conference on Interactive Collaborative Learning, ICL 2012, pp. 1–8 (2012)

    Google Scholar 

  21. Burkhardt, D., Nazemi, K.: Dynamic process support based on users’ behavior. In: Proceedings of 15th International Conference on Interactive Collaborative Learning, ICL 2012, pp. 1–6 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Burkhardt, D., Nazemi, K., Stab, C., Steiger, M., Kuijper, A., Kohlhammer, J. (2013). Visual Statistics Cockpits for Information Gathering in the Policy-Making Process. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41939-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41938-6

  • Online ISBN: 978-3-642-41939-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics