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Exploring the Networks     in Open Public Data                  Uldis BojārsInstitute of Mathematics and Computer Science ...
About us• Institute of Mathematics and Computer  Science, University of Latvia  – http://www.lumii.lv/resource/show/170  –...
Network visualisation and analysisApplications:• discover interesting patterns• explore data in [more] detailWork from the...
Overview• Data needs to be Open• Pre-processing and filtering the data  – selecting what to show• Data visualization  – it...
Open Data needed first (!)“Open data is data that can befreely used, reused and redistributed by anyone …”                ...
http://titania.saeima.lv/LIVS11/SaeimaLIVS2_DK.nsf/0/9DEA96450E79B7E5C2257944007E589D?OpenDocument
Pre-processing• Input:  – raw vote data (scraped from the website)    published at http://data.opendata.lv/• Output:  – no...
Defining graph connections• Connect MPs if they have voted similarly   – disagreed on at most n% of decisions• Filter out ...
Node colour legend• Ruling coalition:   – Zatler’s Reform Party   – Unity   – the National Alliance• Opposition:   – Harmo...
MPs who always vote the same (n = 0%)   Connection criteria too narrow
MPs who disagree in less than 35% of cases      Connection criteria too broad        (everyone agrees, really?)
Refining the visualisation• Need to find the right cut-off values (n%)   – where patterns [start to] appear   – and the vi...
MPs who disagree in less than 11% of casesOpposition parties [sometimes] vote the same
MPs who disagree in less than 25% of cases  Bridges appear b/w position and opposition parties(see slides 21, 22 re the br...
What next?• Improve our understanding of data• Enhance visualisations  – add clusters, etc.• Create multiple visualisation...
network                                         visualisation                                         example #1         D...
network                                       visualisation                                       example #2Intra-company ...
Conclusion• Need more, useful Open Data• Discovering patterns, making sense of data  – helping make sense = purpose of vis...
More info• Uldis Bojārs  uldis.bojars@gmail.com• Social Network Analysis talk / Valdis Krebs  http://www.slideshare.net/DE...
Exploring the Networks in Open Public Data
Exploring the Networks in Open Public Data
Exploring the Networks in Open Public Data
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Exploring the Networks in Open Public Data

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Exploring the Networks in Open Public Data

  1. 1. Exploring the Networks in Open Public Data Uldis BojārsInstitute of Mathematics and Computer Science University of Latvia Using Open Data Workshop Brussels, 20-Jun-2012
  2. 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. 3. Network visualisation and analysisApplications:• discover interesting patterns• explore data in [more] detailWork from the Open Data Hackaton in Riga• analysis of Saeima voting patterns• http://opendata.lv
  4. 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. 5. Open Data needed first (!)“Open data is data that can befreely used, reused and redistributed by anyone …” http://opendefinition.org/Data needs to be:• open• easy to useStill a problem in Latvia:• only a few datasets are open in an easy-to-consume form (PDF does not count :)
  6. 6. http://titania.saeima.lv/LIVS11/SaeimaLIVS2_DK.nsf/0/9DEA96450E79B7E5C2257944007E589D?OpenDocument
  7. 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. 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. 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. 10. MPs who always vote the same (n = 0%) Connection criteria too narrow
  11. 11. MPs who disagree in less than 35% of cases Connection criteria too broad (everyone agrees, really?)
  12. 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. 13. MPs who disagree in less than 11% of casesOpposition parties [sometimes] vote the same
  14. 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. 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. 16. network visualisation example #1 Donations to political partieshttp://www.thenetworkthinkers.com/2011/12/ innovation-happens-at-intersections.html
  17. 17. network visualisation example #2Intra-company communication patterns
  18. 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. 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

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