1. Exploring the Networks
in Open Public Data
Uldis Bojārs
Institute of Mathematics and Computer Science
University of Latvia
Using Open Data Workshop
Brussels, 20-Jun-2012

2. About us
• Institute of Mathematics and Computer
Science, University of Latvia
– http://www.lumii.lv/resource/show/170
– Uldis Bojārs @CaptSolo
– Valdis Krebs http://orgnet.com
– Pēteris Ručevskis

3. Network visualisation and analysis
Applications:
• discover interesting patterns
• explore data in [more] detail
Work from the Open Data Hackaton in Riga
• analysis of Saeima voting patterns
• http://opendata.lv

4. Overview
• Data needs to be Open
• Pre-processing and filtering the data
– selecting what to show
• Data visualization
– iterative process (visualize, refine, repeat)
• What’s next?

5. Open Data needed first (!)
“Open data is data that can be
freely used, reused and redistributed by anyone …”
http://opendefinition.org/
Data needs to be:
• open
• easy to use
Still a problem in Latvia:
• only a few datasets are open in
an easy-to-consume form (PDF does not count :)

6. http://titania.saeima.lv/LIVS11/SaeimaLIVS2_DK.nsf/0/
9DEA96450E79B7E5C2257944007E589D?OpenDocument

7. Pre-processing
• Input:
– raw vote data (scraped from the website)
published at http://data.opendata.lv/
• Output:
– nodes (MPs)
– edges (connections between them)
• What is a connection?

8. Defining graph connections
• Connect MPs if they have voted similarly
– disagreed on at most n% of decisions
• Filter out cases where almost all
MPs voted the same
• Filter out trivial decisions
• Filter out noise

9. Node colour legend
• Ruling coalition:
– Zatler’s Reform Party
– Unity
– the National Alliance
• Opposition:
– Harmony Centre
– Greens / Farmers Party
• a few non-party MPs

10. MPs who always vote the same (n = 0%)
Connection criteria too narrow

11. MPs who disagree in less than 35% of cases
Connection criteria too broad
(everyone agrees, really?)

12. Refining the visualisation
• Need to find the right cut-off values (n%)
– where patterns [start to] appear
– and the visualisation makes sense
• Show the results to domain experts
– MPs, journalists, political researchers, …
• Experts:
– help improve visualisations
– can discover new things for themselves

13. MPs who disagree in less than 11% of cases
Opposition parties [sometimes] vote the same

14. MPs who disagree in less than 25% of cases
Bridges appear b/w position and opposition parties
(see slides 21, 22 re the bridging role of yellow nodes)

15. What next?
• Improve our understanding of data
• Enhance visualisations
– add clusters, etc.
• Create multiple visualisations
– different topics, changes in time, etc.
• Bring in more data
– explain nodes & edges

16. network
visualisation
example #1
Donations to political parties
http://www.thenetworkthinkers.com/2011/12/
innovation-happens-at-intersections.html

17. network
visualisation
example #2
Intra-company communication patterns

18. Conclusion
• Need more, useful Open Data
• Discovering patterns, making sense of data
– helping make sense = purpose of visualisations
• Looking forward to collaboration re:
– Using Open Data
– Data Visualisation and Analysis

19. More info
• Uldis Bojārs
uldis.bojars@gmail.com
• Social Network Analysis talk / Valdis Krebs
http://www.slideshare.net/DERIGalway/
valdis-krebs-social-network-analysis-19872007
• Smart Network Analyzer tool
http://sna.lumii.lv/
in development at IMCS, University of Latvia