# Dissertation – Part 1

Its been a month since I have submitted my dissertation and as promised in the previous posts (http://goo.gl/XqYvuP and http://goo.gl/c7Lvu6 ) now it’s time to start blogging about the dissertation and its outputs. So starting from today I would try to do one post every day  for at least 2 weeks, talking about one aspect of my dissertation, leading up to the final results and conclusions.

To give brief introduction, In my dissertation (titled-“Network theory approach on modelling occurrence of Road Accidents – Case study UK”) I set out to create a dual graph of UK roads, attach the Road accidents data to the graph, find patterns in the distribution of accidents in the dual graph and find correlations between this distribution and the properties of the network.

Today, as a start I want to do a flash-forward into the whole process and share one of the key outputs produced as a part of the dissertation – A static visualisation of the dual graph of the entire road network of UK (England, Scotland and Wales) showing the betweenness centrality of each and every node.

Explanation: The above visualisation shows all the roads in UK as points (simplified by averaging the coordinates of their constituent segments)  and intersections between roads as lines connecting the corresponding points. The size of the points show the betweenness centrality of the corresponding roads in the information space. To put it simply, the visualisation highlights the most central roads in UK when the network is resolved as a dual graph. We can see that M25 is the most central road in the whole network with  a the highest probability of being in a shortest path derived between any two random roads in the network. The visualisation is produced using Processing.

Though it is simple plot of a series of data on a 2D plane, lot of things have been done in the background to create this abstract representation of the UK road network, about which I would be blogging in detail starting from tomorrow. I am hoping this series of posts are interesting to the readers and help me identify all the holes, drawbacks and errors in my research process.