Linked Information Visualization for Linked Open Government Data. A Visual Synthetics Approach to Governmental Data and Knowledge Collections

Authors

  • Florian Windhager Danube-University Krems
  • Eva Mayr
  • Günther Schreder
  • Michael Smuc

DOI:

https://doi.org/10.29379/jedem.v8i2.436

Keywords:

Government data, visual access, information visualization, linked data, linked images, cognition support, macrocognition, civic education, political journalism, linked open government data

Abstract

Open government data initiatives provide citizens with access to the information that governments have about their countries (such as data about people, resources, infrastructure, or  services) upon which they act. Information visualizations can help to make sense of these complex data and knowledge collections, but are mostly used to shed light on subselections of data only, without coordinated efforts to connect them to bigger pictures up to now. In analogy to linked data initiatives, this article discusses methods and strategies to link information visualizations in the government data realm and thereby to connect widely available local pictures and insights into more coherent global mental models. We expect related developments to provide benefits for communication professions like civic education and political journalism, and to open up enhanced methods for cross-domain exploration and reasoning for (linked) open government data. Thus linked information visualizations aim for supporting students, readers, and citizens to meet a widening range of macro-cognitive challenges, which complex societies are facing in increasing amounts.

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Published

19.12.2016

How to Cite

Windhager, F., Mayr, E., Schreder, G., & Smuc, M. (2016). Linked Information Visualization for Linked Open Government Data. A Visual Synthetics Approach to Governmental Data and Knowledge Collections. JeDEM - EJournal of EDemocracy and Open Government, 8(2), 87–116. https://doi.org/10.29379/jedem.v8i2.436

Issue

Section

Special Issue: Research Papers