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Cell tower connections based on contact events

December 10th, 2010 Leave a comment Go to comments

Using the log of hop events, I was able to generate a list of cell towers being used at the time of the hop, from this I built a list of the cell towers that are linked in this way, with a count of the number of times they had been connected (linked_cell list txt, or linked_cells json version ). From this a created a graph where the weight of the edges is the count.

connected-cell-towersEdges with a weight (or number of times nodes passed a message and reported different cell towers) less than 100 are removed for clarity, the colour of the edges shows the weight of the edge increasing from blue to green.

The tool that I have used to visualise these graphs is called the network workbench, and it reports the following about the graph:

Nodes: 84
Isolated nodes: 9
Node attributes present: label
Edges: 149

No self loops were discovered.
No parallel edges were discovered.
Edge attributes:
Did not detect any nonnumeric attributes
Numeric attributes:
min    max    mean
weight     1    2700    103.42953

This network seems to be valued.

Average total degree: 3.547619047619045
Average in degree: 1.7738095238095228
Average out degree: 1.7738095238095228
This graph is not weakly connected.
There are 14 weakly connected components. (9 isolates)
The largest connected component consists of 67 nodes.
This graph is not strongly connected.
There are 61 strongly connected components.
The largest strongly connected component consists of 24 nodes.

Density (disregarding weights): 0.02137
Additional Densities by Numeric Attribute
densities (weighted against standard max)
weight: 2.21041
densities (weighted against observed max)
weight: 0.00082


Edges are coloured linearly from white to black depending on weight, edges with a weight greater than 300 are labelled.

It seems that there are is a group of towers that are strongly connected, by large number of messages being passed, and nodes reporting them as being the cell towers used. One possible reason for this,  is perhaps because the places where the nodes are, are popular, and as such, require more cell towers to cover the demand. These results are promising, because it means that if we pre-process the data, and ignore connections where there is a low weight, we can use groups of towers to give a notion of place. What this means is, when a node reports a tower in the known cluster of towers, we can assume that it is in roughly the same place as any other node who also reports a tower in the same cluster.

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