I have processed more of the Daniel Morgan data, and thus have an updated network of the data. Below is a visualisation of the data produced by extracting the network structure from Neo4J using R and iGraph, then saving the network as a gexf file and importing into Gephi. The network is more complete but also has edge labels.
After listening to the Daniel Morgan podcast, Untold, I became really interested in the murder investigation. To help me follow it I started building a network of all the key people, organisations, and events in the case. The networks this produces can be seen here,and you can keep up-to-date with the progress on the network here.
There is an updated network image here.
The story is a compelling one, I suggest you either listen to the podcast or read the book. Very briefly it looks into the murder of Daniel Morgan, and the subsequent investigations into the murder and the police handling of the murder. The book builds a compelling story of decades of struggle by the Morgan family to get justice, and the difficultly they have had in discovering the truth.
The network is not complete, at the time of writing I have only put in the ‘easy’ bits. The network stores objects as the nodes, so people, companies, organisations. The lines, or edges, store the relationship between the objects, e.g. Alistair Morgan is ‘brother_of’ Daniel Morgan. The visualisation is produced using Alchemy, and the data is stored in Neo4J. I intend to continue to develop the network further, and the visualisation which needs things like edge labels. Once the network is more complete it would be interesting to see if there is any useful analysis that can be done on the network. It would also be interesting to expand the data to include other related and interesting cases. Such as the Stephen Lawrence murder, and the Leveson Inquiry will likely form a part of Algorithmic Indexing in the future.
Here is a picture of the network in Neo4J:
This was an interesting paper that I contributed a section too. It was a look back, and in a sense, a look forwards at four leading management studies journals, ASQ, JMR, JMS and HRM. My involvement was to look at the changing content of the journals in terms of the frequencies of the words being used. Even just looking at the words we were able to separate papers to their publishing journal, and when displayed as a network of correlations papers tended to cluster into journals. This is interesting as it does indicate that journals do have a house style that people inevitably conform to. The causality of how this happens is not clear, it could either be that journals influence how people write, or that people writing about similar things just tend to use similar words and then publish in a sub-set of journals. Further more we where able to look at the changing word use in a single journal through time. Again we were able to see that papers published in different periods of time tended to be most closely related to each other. The full paper is available online, and there is a poster looking at the data also linked below.
I intend to pick this work up again in the near future.
As part of my work exploring the notion of tipping points I did some work looking at abstract models of populations of Banks. This work actually follows on from earlier work (soon to be published in the Journal of Business History) looking at the development of the British Banking sector. It takes a look, through modelling and simulation, at how the banking sector might have developed had history been different ,while trying to contribute to the debate around what a tipping point is and can it be modelled. Modelling tipping points is difficult because the moment you decide that that is what you are doing, then you have already biased your work. You will inevitably build something that is at least capable of undergoing a tipping point. This paper attempts to explore this problem though the lens of banks. Full text, Bursting a Bubble: Abstract Banking Demographics to Understand Tipping Points?