In the days ahead of the first House of Commons \”Meaningful Vote\” to ratify Theresa May’s deal for UK\’s withdrawal from the EU, there was much speculation about whether she would be defeated, and by what margin. The FT sought to produce an accurate estimate showing the range of possible outcomes of the vote based on a manually-compiled dataset containing the best available signals about the Brexit position of all 650 members of Parliament.
The difficulty we had faced in producing graphics to support analysis of the shifting coalitions within the House of Commons in late 2018 had demonstrated that the readily-available data on this subject was insufficient and that we would need to manually assemble and maintain a much richer orignal dataset to continuously classify individual MPs\’ position in the rapidly-evolving debate.
After proving its value by enabling an initial story that included a highly-accurate projection of the likely record scale of the prime minister\’s first defeat, the dataset has been developed iteratively as the subsequent Parliamentary impasse developed, allowing us to feed crucial detail into the daily reporting of the FT\’s Parliamentary reporters, powering further daily stories on the parliamentary arithmetic ahead of key votes (including a precise projection of the prime minister\’s second defeat), and enabling the FT to rapidly deploy interactive visualisations of the outcome of each new set of votes alongside details of each MPs\’ longer-term voting records on Brexit.
Over several months, we have logged MPs’ voting records in the House of Commons, along with their public statements on social media and local news interviews, the signatories to joint letters stating policy positions, and even the membership lists of chat groups used by various factions, allowing us to build up a detailed map of the various Brexit \”tribes\” defining the debate in the House of Commons.
Beyond the initial question of whether or not particular members of Parliament would support the Prime Minister\’s position on Brexit, we began analysing MPs’ voting behaviour in order to visualise the factionalisation of the House of Commons over the issue of Brexit. This was done using analysis of the co-voting networks of all possible pairs of MPs, which was published as part of our “Graphical Insight” series of print graphics, as well as on social media.
The database has developed iteratively, in reaction to the shifts in the story following its initial use in mid-January 2019, and continues to be in use at the date of the DJA awards deadline.
What makes this project innovative?
What was the impact of your project? How did you measure it?
Source and methodology