Ideas of things that could be done with Public Whip data, or similar.
I don't intend to do any of them, as they don't fit my goals - but they
might give you useful suggestions. There are probably a few political
science papers in here.
Some similar things done with US voting data:
Table of most rebellious by party - so see most rebellious Con and LDem.
At the moment only Labour show up.
Table of highest attendance by party - similarly.
Rankings on rebellion/attendance PER PARTY
Compare attendance rates between parties, as they go in and out of
government over the years (use raw data "Extra Turnout" uses)
Compare rebellion rates between parties
Find out must rebellious divisions by party
e.g. find out what issues are fracturing the Tories
Statistics by party - e.g. attendence rate.
How rebellious a party it is. etc.
Proportion of abstentions within each party, may reveal times when MPs abstain in protest.
% of completely pliant MPs in each party, who always follow whip.
Do Co-op members ever vote against Labour as a group?
Academic analyses (of no short term practical political benefit)
Graph of how many MPs defect in each vote over time
Watch for loyalty going down after start of term, to lowest at midterm,
then up again
Analyse if MPs who are "sir" vote differently in anyway
first check data integrity that title always has "Sir" for knights
Get data on gender etc. and analyse against that
Regional analysis. Scotland, NI, Wales, North v South. Urban v. Rural.
Area of land for constituency. This gives a "ruralness" measure.
Population of constituency.
Distance of constituency from London vs. attendance rate
Integrate parliamentary majority, and look for correlations with
rebelliousness? Majorities here:
(Should be no correlation, as reselection more important?)
Plot majority as a colour on the cluster diagram
dONE - no correlation. see website/custom/majority-rebellions.png
First term MPs vs. old warhorses. More rebellious? Less attentive?
Find people who have telled the most times
Cluster distractions (geek fun, but pointless)
Make clustering cope with tellaye/tellno
Make cluster stuff store NUMBER OF VOTES both voted in for extra possible friends info
Chris Lighthead: "I've now written some code to estimate which of the
eigenvectors are significant. The basic idea is that we generate
synthetic data using the marginal distributions for each statements --
that is, like the data which would have been produced by the same number
of respondents as have completed the real survey, but as if their
answers to any one question were unrelated to all the others; and having
done that, we perform the principal components analysis on the synthetic
data. The idea here is that we can compare the eigenvalues from the
synthetic data to the eigenvalues from the real data. If the real
eigenvalue is significantly larger than the one from the synthetic data,
it likely represents real variation in the data; otherwise, random
variation." - we could do this with MP clustering.
Improve clustering distance algorithm
See J Vaughan suggestions
Colour dots in cluster diagram by how many times they have voted.
Bright colours for more relevant the data - i.e. how many intersections
with other's votes there are.
Play with stuff in vector search article
In particular PDL for speeding up octave algebra stuff
> Idea 2. Darren suggested that the reason Tony Blair is an outlier
> in the java app is coz he only turns up to votes he thinks are
> going to be controversial, hence ones that people are probably
> going to vote against him.
Find a metric to see if this is the case.
Make cluster diagram for just divisions relating to one issue. Or
for one person's interested issues. Plot point on cluster diagram for
[I've tried this, with not that useful results - Francis:
- I've made a cluster diagram with just the Iraq votes in.
Unfortunately it isn't really revealing, kind of curious, but I'm not
sure how useful it is in the media. Maybe you have suggestions
otherwise! Find a static screenshot attached.
Anti-war on the left, pro-war on the right. Colours represent
political party. Unfortunately, lots of the dots represent dozens of
MPs when they all voted the same, so the distribution is much denser
to to the right than it at first appears. It's interesting that Con
and Lab still separate out top to bottom... We almost end up with four
Top-left: Anti-war, anti-government
Top-right: Pro-war, anti-government
Bottom-left: Anti-war, pro-government
Bottom-right: Pro-war, pro-government
However, this doesn't really tell us anything we didn't already know. ]
EDM analysis with MDS:
Here's a couple of ideas: a section called "answers that are in the Library".
Also, if we find enough funny business, what about, random question of
the day? People could subscribe to this, and this would get them involved.
Create classification tree so it offers some division questions for you
to pick from, and tells you your party. As does here for EDMs:
Find "Motion made, and Question proposed," and "Question put and agreed
to." when there was no division. Record these "virtual divisions" as
divisions, as they are really, just they were totally uncontested for
whatever reason. Count them up and see where and how many there are,
whether there are more near bedtime etc.
What can we learn from the information that aye/no comes first, and
from the information as to whether government or not government is
aye or no?
Measure lobbying power behind each issue (expenditure by interested
parties). Again, correlate to time spent on it.
Value of the vote. What is the monetary expenditure cost of agreeing
the motion? Graph against time spent discussing, and see how silly the
Measure a sudden drop in attendance rate - so you could see that the MP
used to vote a certain % of the time and now doesn't. This will
detect illness/injury/busy for some other reason.