Earlier this week I attended a “Big Data Investigation Workshop” run by British Library Labs as part of the International Digital Curation Conference.
The workshop was an introduction to working with tools for cleaning, analysing and visualising collections of data: OpenRefine (which is great but showing its age), Tableau (which is ridiculously impressive) and Gephi (which has fast graph layout but lacks usability).
As the workshop was co-organised by the Internation Crime Fiction Research Group, the theme of the data was “Crime Fiction”. However, for our project, we decided to look at “Crime Fact”. In particular, we looked at a recent news story in The Independent, which stated that “three senior figures at scandal-hit [HSBC] bank donated £875,000" to the Conservative Party in recent years.
Although the news story didn’t link to any source data, it almost certainly came from the Electoral Commision’s register of donations to political parties.
Running a basic search of the Electoral Commision’s register, with no filters, produced a CSV file containing all registered donations since 2001, which we then loaded into Tableau Public (Tableau’s limited, free desktop application for data visualisation).
Total donations per party
The first visualisation was a simple bar chart of the total donations to each party, including only “political party” recipients, coloured according to the type of donation.
Total donations per individual
The next visualisation was a summary of the donations from the individuals named in the news story. We added a filter on the donor name, searched for their surname and selected those names which matched (there were several variations on each donor’s name in the database), then used Tableau’s grouping to group together the name variations. Pleasingly the totals almost exactly matched those given in the news story, for the three named donors.
Location of the donors
Getting Tableau to recognise UK postcodes is a bit tricky, as it doesn’t recognise the full postcode - we had to write a function to separate out only the first part of the postcode. Once this was done, Tableau easily mapped the location of each donor, to produce the final visualisation: a map of each donation to a political party, coloured according to the recipient party and sized according to the value of the donation.
