The collaboration network in OSM: the case of Italy.
Maurizio Napolitano<[email protected]>
State of the Map 2013The OpenStreetMap Event
6-8 September 2013Birmingham, UK
How is the collaboration in OpenStreetMap?
What is possible to understand from the data?
Construct the collaborative network
simone modifiesa 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
6Tim moves the point
We have the social directed graph
What we did
Historic openstreetmap of 3cities:
- Trento - Rome - Milan
source code: https://github.com/napo/osmsna/
social graph+ users details
The amazing tools created by Pascal Neis
How did you contribute to OSM? - user EdoM
Who's around me? - Milan City
ABC of SNAby Michela Ferron
http://www.slideshare.net/fbk.eu/fbk-seminar-michela-ferron-presentation
Some social network analisys indicators (1/3)
DEGREE: number of lines incident with a node.
IN-DEGREE: number of lines directed into a nodemeasure of RECEPTIVITY
OUT-DEGREE: number of lines directed from anode to another onemeasure of EXPANSIVENESS
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.
Density of a graph: proportion of possible lines thatare actually present in the graph (the ratio of thenumber of the present lines to the maximumpossible)
measure of COHESION
Some social network analisys indicators (3/3)
HIGH DENSITY LOW DENSITY
• 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:
The three cities
ROMEPeople2.638.842Area1,285.31 km2Density2,100/km2
MILANPeople1.247.379Area181.76 km2Density6,900/km2
TRENTOPeople117.307Area157.9 km2Density740/km2
data & pictures from wikipedia
Rome Milan Trento0
200
400
600
800
1000
1200
OSM history files (Mb)
Population and historic osm data file
Rome Milan Trento0
5000
10000
15000
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30000
Population
956
398315
The social graph - Trento
graph made with gephi
The social graph - Trento
nodes: 289edges: 1169
average degree: 4.05network diameter: 7graph density: 0.014modularity: 0.308 | 71 communitiesNumber of Weakly Connected Components: 64 Number of Stronlgy Connected Components: 136
graph made with gephi
The social graph - Milan
graph made with gephi
The social graph - Milan
nodes: 519edges: 1730
average degree: 3.333network diameter: 8graph density: 0.006modularity: 0.25 | 171 communitiesNumber of Weakly Connected Components: 151 Number of Stronlgy Connected Components: 307
graph made with gephi
Social Graph Milan – users' centroids view
Data calculated using Pascal Neis' tool:“How did you contribute to OpenStreetMap ?”http://hdyc.neis-one.org/
The social graph - Rome
graph made with gephi
The social graph - Rome
nodes: 793edges: 162
average degree: 0,2network diameter: 7graph density: 0modularity: 0.45 | 743 communitiesNumber of Weakly Connected Components: 732 Number of Stronlgy Connected Components: 770
graph made with gephi
Rome - 3D View of the social graph)
A HUGE NUMBER OF CONTRIBUTORS
Dimension of nodes basedon the degree indicator
A huge number of contributors withsmall degree index
Rome – users with high self interaction
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
Summaryfrom the history OpenStreetMap file is possible to extract a social graphthe results of the social network analysis return useful information to understand the community and individual users' behavior
Next stepsimplement longitudinal analyzesextend the analysis to larger regionsimplement a continuous auto-updatedefine an indicator of "crowdquality" in order to provide a level of the quality of data
Conclusion and future work
Thank for your attention!
twitter: @napoblog: http://de.straba.usemail: [email protected]: http://slideshare.net/napo
This work is supported by T2DataExchange – http://trentino.dandelion.eu/a project by Spaziodati Srl, Edizioni Curcu&Genovese, Fondazione Bruno Kesslerwith funds from the European Regional Development Fund