The following is reblogged from the In the Dark blog run by cosmology professor Peter Coles
“The EU referendum campaign may only just have started but already there have been deliberate attempts to mislead the electorate about the realities of EU membership. I know that people will consider a wide range of issues before casting their vote in the forthcoming referendum. I am glad there is to be a referendum because there is at least a chance that some truth will emerge as these topics are discussed publicly over the next four months.”
To read the whole blog post go here: Why the EU is Vital to UK Science
More out of idle curiosity and fun, than because I think this is necessarily an accurate prediction, I thought I’d do some modelling on the possible EU referendum outcome based upon the votes for each party in the 2015 general election.
This is a simple interactive model you can play with yourself. It works by you looking at each of the major parties and you entering what percentage of those who voted for that party in 2015 will vote to remain in the EU referendum.
The model then does its magic by taking that percentage and applying it to the votes cast for that party in 2015 and summing it up to give a nifty graph of the predicted remain / leave vote share. I’ve stuck in my own assumptions to give a win to remain but you can enter your own assumptions in the red text below.
If you want to use it on a phone or tablet you need to double tap one of the red cells to get it to bring up the keyboard.
I just posted a graph of the split of the UK electorate by region and tweeted. Someone asked about other sorts of breakdowns. So I’ve done a table that breaks the electorate down by county area / single tier council.
Someone on Twitter asked (with respect to EU referendum) what the split of the electorate is across the UK regions, so as to determine the most influential regions.
So I did a graph.
In recent days there has been a storm about the recently announced 2016/17 local government grant settlement from central government. The creation of a new £300m relief fund will mainly be used to help Tory-run councils, like David Cameron’s Oxordshire County Council, with Labour leveling the accusation that this is to buy off Tory MPs.
I will leave it to others to form a view on whether this is fair or not. But this whole storm did get me thinking about the scale of the cuts not just for this year, but over the past few years, since the Tories came to power.
So I grabbed hold of the 2011/12 figures for “council spending power” and compared them to the recently announced 2016/17 figures and worked out the percentage cut in spending power for each council. I picked 2011/12 as a base year this was the first full council financial year the Tories were able to fully influence after being elected.
I decided to look only at the 152 County Councils and Single Tier Councils (e.g. London Boroughs, Unitary Councils, Metropolitan Boroughs etc) as they make up over 93 per cent of all council spending. There are 201 district council but they make only about 7 per cent of total council spending. Hence looking only at the “Upper Tier” councils as this made the analysis more focussed.
The thing I wanted to test was the theory that the most deprived councils were worst hit. So I took a trip over to the Office of National Statistics (ONS) English indices of deprivation for 2015 website. Here I got the Index of Multiple Deprivation (IMD) average score breakdown by council areas and then used the rank of the average IMD score to plot percentage change in revenue spending power using versus the IMD average score rank.
Below is the very telling plot of this data. Note councils with low ranks on the IMD (those plotted to the left) are the most deprived and those with high ranks on the IMD (those plotted to the right) are the least deprived.
This graph shows a strong and clear relationship that the councils that are serving the most deprived communities have suffered the largest cuts over the past five years. This very strong relationship is evidenced by the high R2 value (or coefficient of determination) of 0.81. A value of 1 would indicate a perfect fit on the line of best fit, and a value of 0 would mean the data does not fit the line in any way. A value of 0.81 shows a strong and clear fit/relationship.
So there you have it: the numbers don’t lie. The poorest and most deprived have suffered the largest percentage council cuts. The poor have been robbed to subsidise the rich.
If you want to check the data and my calculations you can download it here.
The shapes and colours of this building caught my eye as I walked past it a few days ago.
Two sets of shapes caught my eye this morning as I walked to and from the gym and I managed to get a couple of quick snaps.
First up the green swoosh of the rails coming down from Snow Hill station car park.
Second the geometric patterns on the upper part of the House of Fraser building on Corporation Street.