Spotfire: an information exploration environment
ACM SIGMOD Record
Guidelines for using multiple views in information visualization
AVI '00 Proceedings of the working conference on Advanced visual interfaces
AVI '00 Proceedings of the working conference on Advanced visual interfaces
Exhibit: lightweight structured data publishing
Proceedings of the 16th international conference on World Wide Web
ManyEyes: a Site for Visualization at Internet Scale
IEEE Transactions on Visualization and Computer Graphics
VisGets: Coordinated Visualizations for Web-based Information Exploration and Discovery
IEEE Transactions on Visualization and Computer Graphics
Harnessing the Information Ecosystem with Wiki-based Visualization Dashboards
IEEE Transactions on Visualization and Computer Graphics
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With the rise of the open data movement a lot of statistical data has been made publicly available by governments, statistical offices and other organizations. First efforts to visualize are made by the data providers themselves. Data aggregators go a step beyond: they collect data from different open data repositories and make them comparable by providing data sets from different providers and showing different statistics in the same chart. Another approach is to visualize two different indicators in a scatter plot or on a map. The integration of several data sets in one graph can have several drawbacks: different scales and units are mixed, the graph gets visually cluttered and one cannot easily distinguish between different indicators. Our approach marks a combination of (1) the integration of live data from different data sources, (2) presenting different indicators in coordinated visualizations and (3) allows adding user visualizations to enrich official statistics with personal data. Each indicator gets its own visualization, which fits best for the individual indicator in case of visualization type, scale, unit etc. The different visualizations are linked, so that related items can easily be identified by using mouse over effects on data items.