SPARQL Visualizer

Visualize the Eurovision Linked-Data

Explore the Linked Data stored in this collection through the samples provided that produce a range of visualizations. For a given sample visualization, the approach used is to first load in a SPARQL query that extracts and manipulates the linked data in the underlying triplestore that represents all the metadata stored in the collection. This is then passed on to SGVizler, a JavaScript visualization library, to display in graphical form the generated data.

In terms of operating the user interface, this is achieved by pressing the Load query above button for a sample visualization that has piqued your interest (listed below). This causes a Visualize Results button to appear alongside the load button you have just pressed, which when clicked runs the loaded-in query and visualizes the result.

We use this two-step process so it is possible to change what query is run, and how the resulting data is visualized. The first text-box below is for the SPARQL query. The following 3 text-boxes control aspects of the visualization. If you haven't worked with the underlying tools before, we suggest you work your way through the sample visualizations provided, trying out small edits to see how that affects what is produced.

Rather than visualize results, if you would like to directly access and/or export the data to peroforms other forms of analysis, then you'll probably want to use the:

SGVizler Query

Loading ...

Sample Visualizations

The samples provided below show a range of visualizations related to the Eurovision Song Contest, sourced from the raw data in the triple-store, and converted into a visualization using SGVizler. Click on one of the Load query above buttons to load the relevant query syntax into the SPARQL Query textbox above, accompanied with SGVizler attributes that control the visualization, then press Get Results to initiate the visualization.

  • Number of times entered, sorted by frequency:

    Plot as a bar graph the number of times each country has competed in the Eurovision Song Contest, sorted by frequency.

  • Made the Finals:

    Plot as a bar graph the number of times each country has made it to the finals.

  • List of Winners:

    The songs that have won through the ages.

  • List of Last Place Entrants:

    The songs that have won through the ages.

  • Top 3 Acts per Year with (where available) Details of Musical Content:

    List the Top 3 entries per year, including musical details such as tempo, time-signature, and key where alignment with content in MusicBrainz was possible.

  • The ignominy of "nul point":

    Plot a bar graph showing which countries, and how often they have, competed in the final but received zero points as their final total score.

  • The even more galling circumstance of getting "nul point" having won the previous year:

    Have any countries ever been in the situation of going from Hero (i.e., winning) to Zero (nul point) in back to back years in the contest.

  • Rate of occurrences of "nul points":

    Plot a bar graph showing the distribution by year of how often countries have ended up with zero point.

  • Dataflow Voting Patterns of Juries:

    Plot as a Sankey Dataflow Graph how juries allocate their votes to countries over the years 2010-2019. Note: to help emphasize the voting patterns, we plot the square of the voting totals.

    Experiment with editing the date range specified in the Start Year and End Year text-input boxes. Alternatively, the the SPARQL query itself edit the FILTER(xsd:integer(?year) > 2010 && xsd:integer(?year) <= 2019) clause in the above text box and then press Show Visualization to see how the voting by jury has changed over the decades.

  • Dataflow Voting Patterns in Televoting:
    Start Year: End Year:

    Plot as a Sankey Dataflow Graph of the televoting data by country. Years plotted are 2016-2019 as these are the only years the data has been published separate to jury voting. As with the Jury Sankey visual above, we plot the square of the voting totals to help emphasize where differences occur.

  • Differences between Jury and Televoting:
    For the country: In the year:

    Plot as a Tree-map how the various juries have cast their votes for a specific country. The area of the block represents the number of points awarded by the jury. To this we add colouring to the block to show how much in agreement the televote was: red indicates the televote was higher than the the jury vote; green is when the votes are similar (neutral); and blue indicates the televote was lower then the jury vote.

  • The Curse of Being the Second Performer in the Lineup?

    Plot as a bar graph how many times an entrant performing in a given position in the lineup has gone on to win the competition. Performing second in the line-up historically has been considered an unlucky draw position to perform in, but are there any others? Any draw positions that seem to prove favourable to winning?

  • Normalized Plot of ... The Curse of Being the Second Performer in the Lineup?

    Same as the above, only to better take account of the fact that an increasing number of countries have been taking part in the final, in this version of the plot the values have normalized to express as a percentage the number of times an entrant has won in that position of the draw, as a percentage of the total number of times there has been an entrant performing at that position of the draw.