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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.
Maurizio Napolitano<napo@fbk.eu>
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
20000
25000
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: napo@fbk.euslide: 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
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