### GoMore Network Analysis - Online Geospatial Education Program

```GoMore Network Analysis
Kate Lyndegaard
GEOG 596A
Mentor: Frank Hardisty
Outline
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Motivation and summary
What is network analysis?
The project objective
The GoMore network
The preliminary results
The next steps
Thanks
Motivation and Summary
Motivation and Summary
-
How can we begin to discuss integration of spatial analysis and
social network analysis intersect?
-
Are there elements of geospatial/geostatistical theory which
complement social network theoretic approaches to examining
context and relationships in networks, and vice versa?
Motivation and Summary
-
How can we begin to discuss integration of spatial analysis and
social network analysis intersect?
-
Are there elements of geospatial/geostatistical theory which
complement social network theoretic approaches to examining
context and relationships in networks, and vice versa?
What is network analysis?
Network Representation
A network represents
data as a series of
‘nodes’ and ‘edges’
Node
Edge
Directed Networks
A directed network
represents the
directionality of events
represented by ‘edges’
Weighted Networks
Not all networks are
represented by edges
sharing equal values.
15
Weighted networks
every edge.
6
2
2
2
15
10
2
Topological Properties: Degree
Centrality measures
such as ‘degree’
measure the relative
importance of a node
within a network.
Degree = 5
Out degree = 3
In degree = 2
Out degree = 1
Source: Lotan, G.: Network X and Gephi, NYC PyData Conference 2012
Topological Properties: Path Length
Path length measures the
number of edges between
two nodes.
Shortest path length, and
average path length
(calculated over the entire
graph), may also be
measured.
1
2
3
4
5
6
Path Length = 2
The Project Objective.
Analyze the GoMore Network
• Analyze the network of Northern Europe’s leading, online ride-share
provider: GoMore (www.GoMore.dk)
Analyze the GoMore Network
1. Examine a number of topological network properties in
order to characterize the interconnectivity of GoMore
travel
2. Examine weighted network properties in order to
analyze the significance of the volume of travel
3. Determine whether or not there is a correlation between
population density and the volume of travel between
network nodes
The GoMore network.
Creating the Network Graph
Technologies employed:
• PostgreSQL: Advanced SQL queries to structure data
• Gephi: Generate .GML (graph file), visualization,
analyses
• igraph as R package: Implement network analysis
algorithms
• R: Implement statistical methods, visualization
• ArcGIS for Desktop: Spatial analyses, visualization,
feature attribution, processing of demographic data
Creating the Network Graph:
PostgreSQL
‘nodes’ table
Creating the Network Graph:
PostgreSQL
‘edges’ table
Creating the Network Graph: Gephi
GoMore network graph: Circular Layout
Creating the Network Graph:
ArcGIS
Creating the Network Graph:
ArcGIS
The Preliminary Results.
Topological Properties of the
GoMore Network
Comparative Model: Erdős-Rényi
random graph
Power Law Degree Distribution
Clustering Spectrum: Average Local
Clustering Coefficient vs. degree
Source: Barabási, A.L.: Network Science. Forthcoming, pp. 41
Average Nearest Neighbor vs.
Degree
The Next Steps.
The GoMore Network and
Population Data
Next Steps
1. Complete and refine weighted network analyses
2. Refine spatial analyses
3. Discuss correlations between network and spatial
analyses
4. Complete journal article for peer-review (GIScience
2014, Vienna, Austria)
References
Albert, R., Barabási, A. L.: Topology of evolving networks: local events and universality. Physical review letters 85.24, 5234
(2000).
Barabási, A.L.: Network Science. Forthcoming.
Barabási, A.L., Albert, R.: Emergence of Scaling in Random Networks. Science 286.5439, 509-512 (1999)
Barthélemy, M., Barrat, A., Pastor-Satorras, R., Vespignani, A.: Characterization and modeling of weighted networks. Physica
a: Statistical mechanics and its applications 346.1, 34-43 (2005)
Barrat, A., Barthelemy, M., Pastor-Satorras, R., Vespignani, A.: The architecture of complex weighted networks. PNAS 101.11,
3747-3752 (2004)
Clauset, A., Shalizi, C. R., Newman, M. E.: Power-law distributions in empirical data. SIAM Review 51.4, 661-703 (2009)
De Montis, A., Barthélemy, M., Chessa, A., Vespignani, A.: The structure of inter-urban traffic: A weighted network
analysis. arXiv preprint physics/0507106 (2005)
De Montis, A., Caschili, S., Chessa, A.: Spatial Complex Network Analysis and Accessibility Indicators: the Case of Municipal
Commuting in Sardinia, Italy. EJTIR 4.11, (2011)
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network analysis: A study of the Italian islands of Sardinia and Sicily. Journal of Transport and Land Use 2.3, (2010)
Easley, D., Kleinberg, J.: Networks, crowds, and markets. Cambridge, Cambridge University Press (2010)
Erdős, P., Rényi, A.: On the evolution of random graphs. Magyar Tud. Akad. Mat. Kutató Int. Közl 5, 17-61 (1960).
Gallego, F. J.: A population density grid of the European Union. Population and Environment 31.6, 460-473 (2010).
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Patuelli, R., Reggiani, A., Gorman, S. P., Nijkamp, P., Bade, F. J.: Network analysis of commuting flows: A comparative static
approach to German data. Networks and Spatial Economics 7.4, 315-331 (2007)
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Thanks
I would like to thank Beth King and Frank Hardisty
```