The collaboration network in OSM - the case of Italy


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Data quality can be evaluated with a knowledge of the processes underlying the production of
the data. Understanding users' interactions is a necessary step to find which areas are the most curated in OpenStreetMap. Extracting information from each user's contribution history it is possible to find the users' interaction network and their preferred area of activity. In this presentation we want to show how social network analysis over a given area can be performed to obtain a "collaboration score" for a single user and we present our work on the analysis of the OSM users' social network for Italy.

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The collaboration network in OSM - the case of Italy

  1. 1. The collaboration network in OSM: the case of Italy. Maurizio Napolitano <> State of the Map 2013 The OpenStreetMap Event 6-8 September 2013 Birmingham, UK
  2. 2. How is the collaboration in OpenStreetMap? What is possible to understand from the data?
  3. 3. Construct the collaborative network simone modifies a tag made by Tim SteveC adds a point simone adds a tag 1 2 3 4 tim assigns a name to a street drawed by simone 5 SteveC adds a tag 6 Tim moves the point
  4. 4. We have the social directed graph
  5. 5. What we did Historic openstreetmap of 3 cities: - Trento - Rome - Milan source code: social graph + users details
  6. 6. The amazing tools created by Pascal Neis How did you contribute to OSM? - user EdoM Who's around me? - Milan City
  7. 7. ABC of SNA by Michela Ferron
  8. 8. Some social network analisys indicators (1/3) DEGREE: number of lines incident with a node. IN-DEGREE: number of lines directed into a node measure of RECEPTIVITY OUT-DEGREE: number of lines directed from anode to another one measure of EXPANSIVENESS
  9. 9. Some social network analisys indicators (2/3) An actor has a high betweenness centrality if he/she lies between many of other actors (technically, on their geodesic) Prominence = “CONTROL ON COMMUNICATION” BETWEENNESS centrality: Interactions between two nonadjacent actors might depend on other actors, who might have some control over the interactions of the others.
  10. 10. Density of a graph: proportion of possible lines that are actually present in the graph (the ratio of the number of the present lines to the maximum possible) measure of COHESION Some social network analisys indicators (3/3) HIGH DENSITY LOW DENSITY
  11. 11. • DEGREE: level of activity in the community • IN-DEGREE: level of corrections received • OUT-DEGREE: level of corrections made • BETWEENNESS: level of collaboration in the community • DENSITY: community cohesion indicator In the case of the OpenStreetMap users:
  12. 12. The three cities ROME People 2.638.842 Area 1,285.31 km2 Density 2,100/km2 MILAN People 1.247.379 Area 181.76 km2 Density 6,900/km2 TRENTO People 117.307 Area 157.9 km2 Density 740/km2 data & pictures from wikipedia
  13. 13. Rome Milan Trento 0 200 400 600 800 1000 1200 OSM history files (Mb) Population and historic osm data file Rome Milan Trento 0 5000 10000 15000 20000 25000 30000 Population 956 398 315
  14. 14. The social graph - Trento graph made with gephi
  15. 15. The social graph - Trento nodes: 289 edges: 1169 average degree: 4.05 network diameter: 7 graph density: 0.014 modularity: 0.308 | 71 communities Number of Weakly Connected Components: 64 Number of Stronlgy Connected Components: 136 graph made with gephi
  16. 16. The social graph - Milan graph made with gephi
  17. 17. The social graph - Milan nodes: 519 edges: 1730 average degree: 3.333 network diameter: 8 graph density: 0.006 modularity: 0.25 | 171 communities Number of Weakly Connected Components: 151 Number of Stronlgy Connected Components: 307 graph made with gephi
  18. 18. Social Graph Milan – users' centroids view Data calculated using Pascal Neis' tool: “How did you contribute to OpenStreetMap ?”
  19. 19. The social graph - Rome graph made with gephi
  20. 20. The social graph - Rome nodes: 793 edges: 162 average degree: 0,2 network diameter: 7 graph density: 0 modularity: 0.45 | 743 communities Number of Weakly Connected Components: 732 Number of Stronlgy Connected Components: 770 graph made with gephi
  21. 21. Rome - 3D View of the social graph) A HUGE NUMBER OF CONTRIBUTORS Dimension of nodes based on the degree indicator A huge number of contributors with small degree index
  22. 22. Rome – users with high self interaction
  23. 23. comparison results Social Netwok Analysis TRENTO MILAN ROME nodes 289 519 793 edges 1169 1730 162 graph density 0.014 0.006 0 modularity 0.308 0.250 0.45 communities 71 171 743
  24. 24. SNA metrics and more for a single user
  25. 25. Summary from the history OpenStreetMap file is possible to extract a social graph the results of the social network analysis return useful information to understand the community and individual users' behavior Next steps implement longitudinal analyzes extend the analysis to larger regions implement a continuous auto-update define an indicator of "crowdquality" in order to provide a level of the quality of data Conclusion and future work
  26. 26. Thank for your attention! twitter: @napo blog: email: slide: This work is supported by T2DataExchange – a project by Spaziodati Srl, Edizioni Curcu&Genovese, Fondazione Bruno Kessler with funds from the European Regional Development Fund