In this talk I will show how Visualbox can facilitate the creation of Linked Data visualizations and how this can be useful in government.

1. Creating visualizations
using Linked Data
Alvaro Graves
gravea3@rpi.edu
@alvarograves
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2. Agenda
• Visualizations based on Linked Data
• Why they are useful?
• Challenges
• What is Visualbox
• Examples
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3. Linked Data Visualizations
• Why governments should publish Linked Data?
• Structured data on the Web
• Flexible to model multiple domains (health,
education, budgets)
• Open, machine-friendly standards to publish data
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4. Linked Data Visualizations
• Publishing is not enough!
• How do we make easier for users to consume it?
• How do we explore the data?
• How can we discover trends?
• How can we find errors or outliers?
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5. Linked Data Visualizations
• Why visualizations are useful?
• In many cases, visualizations make it easy to
consume data
• It helps to discover and explore trends in a
simply manner
• Errors that otherwise would be hard to find,
can be found easily
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6. Detect anomalies
Attraction in Berlin?
http://visualbox.org/demos/AttractionsInBerlin
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7. Discover trends
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8. Challenges
• Using visualization tools is hard
• Many technologies
• JavaScript, Google Maps, SVG, D3.js, etc.
• Steep learning curve
• It’s not easy to create rapid prototypes
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9. Visualbox
• Environment for creating LD visualizations
• Provides multiple visualizations types
•Maps, graphs, charts, timelines
• Simple creation process
• Trivial to share (just pass a URL)
• http://myserver/myVisualization
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10. GUI Editor
1 Components list 3 Template Editor
Query and
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visualization
helpers
7 Show results 2 Template Editor 5 Testing options
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6 Testing results
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11. How do you create a
visualization in Visualbox?
Step 1: Create a SPARQL query
SELECT DISTINCT ?country ?population WHERE{
?country ex:hasPopulation ?population
}
Step 2: Add a visualization filter
{{models.main|GoogleVizColumnChart:"country,population"}}
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12. Example: Trends in
UK school system
Step 1: Create a SPARQL query (called main)
SELECT DISTINCT (SUM(?c) AS ?schoolCapacity)
(COUNT(?school) AS ?schoolsPerDistrict)
(SUM(?c)/COUNT(?school) as ?ratio)
(MAX(?districtl) AS ?l)
WHERE{
?school school:schoolCapacity ?c ;
school:districtAdministrative ?district.
?district rdfs:label ?districtl .
}GROUP BY ?district
ORDER BY ?schoolCapacity
Step 2: Add a visualization filter
{{models.main|GoogleVizColumnChart:"l,schoolsPerDistrict"}}
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13. {{models.main|GoogleVizColumnChart:"l,schoolsPerDistrict"}}
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14. {{models.main|GoogleVizColumnChart:"l,ratio"}}
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15. Average school size vs. number of schools per district
{{models.main|GoogleVizColumnChart:"districtLabel,schoolCapacity"}}
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16. Average school size vs. number of schools per district
{{models.main|GoogleVizColumnChart:"districtLabel,schoolCapacity"}}
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17. Further questions
• What if we have
• Average scores in Math, Language, etc.?
• Poverty level of each area?
• Other social metrics
• Compare to other countries?
• Linked Data makes it easy to
query and mix data
• Visualbox makes it easy to
visualize it
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19. More info
Visualbox:
http://visualbox.org
Demos:
http://visualbox.org/demos
Github:
https://github.com/alangrafu/visualbox
Visualbox Virtual Machine:
http://bit.ly/visualboxVM
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