Monday, 12 June 2017

General Election 2017: some maps and data

It's 2017 and there has just been a General Election in the UK. In case you're reading this in the future, I'm talking about the first General Election of 2017. Today's post is a brief comment, plus a few maps, starting with one on the Labour win in Kensington in London. To summarise briefly: Labour won a seat that many people thought they couldn't, and won by 20 votes with Emma Dent Coad becoming the new MP in a gain from the Conservatives. 

But this is an immensely wealthy area, isn't it? Yes and no. There is a lot of money here but also a good deal of poverty, as you can see from the map below, where the red areas are among the 20% most deprived in England. As with most things, however, it's not a simple story and the result is perhaps not that shocking when you look at the map, even if some have named it the 'UK's richest constituency' (and there is an argument for that view). It's also definitely not 'London's Richest District', as one report puts it.

Kensington is a very different area north to south - click to enlarge

Some numbers, to help put things in context... If we rank the 533 English constituencies by deprivation, using the Indices of Deprivation 2015 measure calculated by the House of Commons Library, the Kensington constituency ranks 178 out of 533 - just on the edge of the most deprived third in all of England. One look at the map tells us that within the area there is considerable variation, with some parts much more deprived than the national average and some parts less so. In total, 22 of Kensington's small areas (LSOAs) are in the 20% most deprived in England - and none are in the 20% least deprived. The picture is very similar if you look at other indicators, particularly those related to income.

However, in London and beyond, the name 'Kensington' has it seems become synonymous with wealth, opulence and 'the elite'. This may be part of the Kensington story, but it's by no means the full story. I just wanted to take a closer look at what the data say in order to figure out if this is really such a surprise. As I did so, I also homed in on a few other constituencies. In addition, I attempted to look at the correlation between Conservative vote share and level of deprivation by constituency. I did this for 2015 and 2017 and although the results are not that surprising - i.e. the more deprived an areas is, the lower the Tory vote - some of the outliers are quite interesting. Here are the two charts - below. I've labelled some of the interesting ones as you can see below. 

A pretty strong relationship in 2015, as you'd expect

The correlation is a bit weaker here - everyone has ideas about why

Let's take a look at Sheffield Hallam because it's such an outlier - and also I live in Sheffield (but not there). It was Nick Clegg's constituency but is now Labour (Jared O'Mara MP). It's also the 9th least deprived constituency in England so is something of an anomaly, though of course not so much when you look at voting history and demographics. Still, the map is interesting in itself - it bucks the general pattern of the least deprived areas voting Conservative.

It has a high student population, among other factors

I then wanted to look at a couple of other places to see what things looked like on the ground. First up is Islington North (Jeremy Corbyn MP) and Islington South and Finsbury (Emily Thornberry MP). Note the little patch of blue around Highbury in an otherwise quite deprived area. This also contrasts with stereotypes of Islington as some kind of land of milk and honey. There is considerable wealth here but it sits beside large areas of deprivation.

Note the little blue patch near Highbury

Then I looked at the most deprived Conservative-voting constituency. This is currently Walsall North, which is ranked 31 out of 533 constituencies on the deprivation measure. Again, there are local and historic explanations for this but I don't want to dwell on those now.

Surprising? Maybe, maybe not.

Last of all, I wanted to look at a constituency where a Labour win truly would be a shock - for this, I looked at the least deprived constituency in all of England: North East Hampshire. When Labour win here I think we can all agree that it would be a shock, just as if the Conservatives won Walton in Liverpool (85.7% Labour, 8.6% Conservative in 2017). Actually, the latter might just be the biggest shock in the history of the world. Mind you, these days you never know what's going to happen next.

The Conservatives got 65.5% of the vote here.

Addendum: I saw a great histogram by Owen Boswarva looking at party by median age in each constituency so I attempted something vaguely similar for deprivation deciles and party. It's not at all surprising but I did find it interesting so am posting it here too.

Click to enlarge - the pattern is to be expected, but quite interesting

Notes: I do know, of course, that there will always be a strong linear relationship between deprivation and % voting Tory - or Labour for that matter. The point here is that because this is true, and because Kensington is actually quite deprived, the result there is less of a shock than some are claiming. Also, ranking deprivation at the scale of constituencies masks lots of underlying variation - but that's partly why I mapped it at LSOA. If we had LSOA General Election results that would be interesting. The scatterplots were interesting to me not because of the obvious linearity but because a) the relationship changed a good bit between 2015 and 2017 (UKIP effect?) and b) the big residuals - e.g. Sheffield Hallam. The ones which defy the general pattern are the ones I'm interested in. Basically, Kensington is right on the trend line and maybe it's because many more people from the deprived parts of Kensington voted this time - plus rich remainers. Finally, a lack of deprivation is not the same as affluence but on any measure you'll find they correlate strongly.

Wednesday, 24 May 2017

The Great Polish Map of Scotland (aka The Mapa Scotland)

Last week I was in Edinburgh to give a talk at the Edinburgh Earth Observatory seminar series at the University of Edinburgh, so I thought I'd try to see The Great Polish Map of Scotland - also known as the Mapa Scotland - before heading back down south. As you can see below, I did manage to go, but I am not quite tall enough to get a good view of it, so I have embedded a video below to give you a proper view of what it's like. I've also posted photos of the very informative signs that have been put up, in addition to a few more views. It's still being renovated so perhaps I didn't visit at the best of times but I'm really glad I got to see it - it's said to be the world's biggest topographical map.

The Great Polish Map of Scotland is located in Eddleston in the Scottish Borders, about 45 minutes south of Edinburgh. When I went on a Saturday morning with an old friend the roads were pretty quiet but it's definitely reachable in under an hour either way. You can see the location in the map below.

Just a short drive and you're there

The Map is actually in the grounds of Barony Castle Hotel, and when we went we parked up in the hotel car park. Above the front door of the hotel you'll see a very ominous message - "Prepare to Meet Thy God" - but since I've not got to the bottom of that yet, I'll leave it there for now and just show you what the Map looks like in the photos below.

Yes, welcome to our hotel!

You go round to the left side of the hotel and then follow the signs to Maczek's Map, as you can see from the next two images. Then it's through the gate and across the bridge over Dean Burn (in case you didn't know, in Scotland and some other parts of the UK we tend to call a stream a 'burn').

I think that's Maczek rather than MacZek

Almost there - you can see the bridge here too

Okay, once you're at the Map you'll see nice new green railings surrounding it. I'm reliably informed by Addy Pope - great Scottish adventurer, ESRI boffin and local person - that you used to be able to walk all over the map but given the new fence and ongoing restoration I thought that might now be frowned upon, so I stayed on the right site of the fence. I'm going to be a bit controversial now on two points. First, I was a little disappointed. Not by the map, but by the fact that I couldn't get a better view of it. I'm close to 2 metres tall but that's not enough even when you're on top of the newly constructed viewing platform. That brings me to the second point. I really wish the viewing platform was higher. These are kind of unfair things to say given the excellent restoration work going on but I do hope someone reads this and gives them tons of money to build a 50m high viewing tower. That would be amazing. Planning permission might be an issue.

This makes a big difference - I just wish it was higher
A couple of views of the map now follow. The first was taken from the viewing platform and the second from the west side of the map. At this point I should probably say that it's not technically The Great Polish Map of Scotland in the sense that Orkney and Shetland are missing. I'm from the north of Scotland so I notice these things... I can understand the omission though - what is there now took years to build.

This was as high as I could get my camera

Extra points if you recognise where this is
One of the great things about the Map is the information signs all the way round that give you the history of the map. I've taken photos of all of them so hopefully you can click the images to read the text but I've also done a few zoomed-in ones, just in case.

Inspired, of course, by a 1958 map of Belgium

I didn't know about this

A close up from the image above

A few images of construction

How the map was made - closer view above

Scotland and Poland have many connections - see above

As you can see, North Uist remains under cover (Uist = "you-ist")
I wasn't really complaining about the viewing platform itself - it's a great addition and allows you to get a nice overview of the Map - but I do think it would be a much better experience if the tower could be higher. I'm sure everyone thinks that and it's such an obvious, annoying thing to say. Anyway, I took this image to show how it was funded.

Lottery funding for the viewing platform

And that's it. I am very glad I went, but it would have been better if a) it wasn't raining - though this is always a risk in Scotland and b) I could fly. In that respect, I think the best views of this are to be had by the few drone videos on YouTube, one of which is posted above. Finally, despite the ominous sign at the entrance of the hotel, I can confirm that they actually serve a very good cup of tea and there wasn't even a hint of death. In fact, the staff were most welcoming.

Do widzenia (for now).

Sunday, 7 May 2017

General Election 2015: the view from second place

In my last blog post I shared a shapefile with the current UK constituency boundaries, which included a lot of other data. One of the variables included was who came second in the 2015 UK General Election. I thought it would be interesting to map this and also include a couple of widgets using the new Builder tools in CARTO (formerly CartoDB). I wanted to do this because I knew UKIP came second in 120 constituencies and I wanted to see where. I also wanted to post an interactive version of the data from my shapefile so people could explore it themselves. The first map below shows who came second in each constituency in 2015 and if you click an area you'll get more information - winner, MP, and so on. Using the widgets below you can then select by winning party and margin of victory, should you want to quickly identify marginal seats, for example.

Here's what the pop-up looks like

In the next map, I've used the 'Majority in 2015' widget to select only those areas with a majority of 3,000 or less and this then updates the 'Winner in 2015' widget so that you can see 41 of these constituencies voted Labour in 2015 and 36 were Conservative.

Many of these could be considered true marginals

At the other end of the scale, I then used the widget slider to select all those constituencies in 2015 which had a majority of 15,000 or more. The final map below shows this. As you can see, 153 of these were Conservative constituencies and 59 were Labour. The colours on the map - remember - are who came second in 2015. So is this a 'no chance of winning here' map? Possibly. I wouldn't be holding out for any shocks though.

Fiddle around with this map here

Here's what the map looks like when you show Labour, Conservative and then UKIP second place finishes.

Labour came second in 253 constituencies in 2015

The Conservatives came second in 181 constituencies in 2015

UKIP came second in 120 constituencies in 2015

I didn't make this so that I could comment on it so have a go yourself in the full screen version.

Sunday, 23 April 2017

Getting ready for #GE2017 - a big shapefile

I'm probably as unmoved as anyone else about the forthcoming General Election, but to get my head back into gear for it I thought I'd try to put together a full UK constituency shapefile of all 650 constituency results from the 2015 General Election, using data from a variety of sources. I'm sharing it here in the hope that people will find it useful, and that it might save you some work. If you spot an error, let me know and I'll try to fix it. There are other shapfiles out there, but to my knowledge there isn't a detailed complete UK (as opposed to GB) file that has all results, MPs and so on. I'm also sharing this here in the hope that we can move away from hex maps. I think they are nice and useful in many cases but I'd like to see a move back to the standard geographic representation in this election - hence, I am trying to promote Hexit. Anyway, here's an obligatory geogif I made with the file, using the 'time results declared' field.

The 2015 General Election in 30 seconds - phew

So, what's in the file? Well, I've tried to include a lot of stuff, sourced variously from the British Election Study, from the UK Parliament Data website, the Census and the devolved administrations of the UK. I have also calculated some variables myself, such as constituency area and the order in which results were declared. Key variables include:

  • PCONCODE - this is the ONS code for each constituency. It makes it possible to join lots of other data to the file. 
  • REGN - name of the sub-UK region each constituency is in - i.e. the old Government Office Regions in England, plus Northern Ireland, Scotland and Wales.
  • SECOND - which party came second in a constituency in 2015.
  • ELECT15 - the number of people in the electorate in 2015.
  • MAJ - size of the majority for the sitting MP.
  • TIME - time the results were declared. The very last column has this in 24H format, but you can also see from the ORDER2015 field which order they are actually in.
  • MPFIRST, MPLAST, MPNAME - the first, last and full name of each MP.
  • Winner15 - this contains the full party name of the winning party. The WINNER field contains the abbreviated party name.
  • POP2015 - this contains the mid-year population estimate for each constituency for 2015. I also added in the 18+ population, since it makes a bit more sense to do this, even though it is not the same as the electorate figure. 
  • Others - they should be self-explanatory but the list of Sources below will help if you are confused by any of these.

I hope you find this useful. If you want to download it, it can be accessed here. If you spot any glaring errors, please let me know. Who is going to win the 2017 General Election? My only prediction is that there will be lots of interesting maps and that the patterns on them may look a bit different.

Data notes: I have added a QGIS qml style file to the zipped data folder. This means that if you add the shapefile to QGIS it will display in the familiar colours of each political party. This happens because the qml file has the same name as the shapefile. The colours are matched from the BBC election results page from 2015. I tried very hard to ensure complete UK coverage, so I have patched data together from multiple UK sources but in a few cases I don't have variables for Northern Ireland. This is because the spreadsheet from the British Election Study I sourced some data from covers only GB. The zipped folder name for the current file version is uk_650_wpc_2017_full_res_v1.8.

Sources: General Election 2015 results, from the UK Parliament Data pages. The British Election Study updated Excel file. Northern Ireland constituency boundaries were sourced from OpenDataNI, via their resources page. For Great Britain, I used the constituency boundaries available on the ONS Geography Portal pages - the 2016 boundaries. For the most recent mid-year population estimates, I used data from the National Records of Scotland, NISRA data for Northern Ireland mid-year population estimates and ONS mid-year population estimates for England and Wales. The map data contains OS data © Crown copyright and database right 2017. Similarly, the other data contains National Statistics data © Crown copyright and database right 2017.

Acknowledgements: I would like to thank Ian Turton for suggesting the little QGIS Atlas function tweak which enables the cumulative animation you see above. For more on this, see the related Stack Exchange post where I asked the question.

Friday, 31 March 2017

Visualising a lot

This post is about visualising 'a lot', because it's something I've been thinking about as I write part of a book on GIS. The basic idea I'm exploring here is that when you have a dataset and want to somehow simply visualise 'a lot' - e.g. because the volume of data seems overwhelming - then there are different ways to approach it. For example, if you had millions of points on a map, you could use a hex-binning technique to give a standardised per-area figure, or you could do some kind of visual aggregation or summary in chart form. Or, to convey 'a lot' as a kind of visual device, you could perhaps just do a visual data dump, as I did in this example. Today's 'lot' is from the Gun Violence Archive dataset for the United States in 2015, compiled and released by The Guardian and collated by the Gun Violence Archive. I opted for a fast animation to visualise 'a lot', which I have now updated with a running total (in yellow). Let's go straight to the gif now, showing all gun homicides, one frame per day, for 2015 (and fast - 10 frames per second).

It's supposed to be overwhelming - click it for full size

When I looked at the original dataset at first, which includes, more than 13,000 gun deaths, my immediate thought was 'that's a lot'. All things are relative, of course, but in a global context it's hard to argue against this, particularly when you compare the data to other developed nations. The dataset has precise lat/long details for each incident and also the date and number killed and injured. I then summarised the data by day, plotted the locations as single points and then created 365 frames for this animated gif. It's not supposed to be readable at the micro scale of individual days or incidents, because I wanted to focus attention on the volume of data. A video version that you can pause or play more slowly is embedded below. I also did a slightly slower animated gif, at 5 frames per second, which of course is still somewhat overwhelming, shown below. Update: I have also added a cumulative version, prompted by Simon Rogers, and thanks to a bit of help from Ian Turton.

This is the same as above, but a little slower (73 seconds in total)

In this version, it's cumulative - click to enlarge and start from beginning

The individual frames were created in QGIS and in relation to the max and min values per day you can see those below. The largest number of gun deaths in a single day in 2015 was on July 5th and the lowest was on May 22nd. The mean number killed per incident was 1.12 and the mean per day in 2015 was 35.8 (for a total of 13,067).

The peak month overall in 2015 was also July

This was the only day that the number killed was below 20

There are just over 11,600 incidents recorded in the database but it's quite difficult to get your head around at a national scale. The Guardian already published some great localised mapping of this data, if you're interested. With this example I was just trying to experiment with ways that quickly and simply convey the idea of 'a lot'. The fast animation using thousands of data points is one way of doing this. It's designed with repetition and replay in mind, and the point is not to highlight individual datapoints or days, but to create a kind of cognitive mash where the end result is that you can take away some detail - e.g. most days have between 20 and 50 gun deaths - and also see the locations do, as you'd expect, mirror underlying population patterns. But only to a point. If you look closely you can see that some places are over or under-represented.

There are many ways to powerfully visualise this kind of data, including much more nuanced interactive methods of the kind produced by FiveThirtyEight. My approach here is non-interactive on purpose, but of course it is less visually appealing too. But then I also think that making something beautiful out of something so ugly is not what I want to be doing. All I wanted to achieve was to highlight the volume in the data in a way that anyone could understand and by using one frame per day and plotting the location points I think I'm just about there.

If you're interested in looking at any of the individual frames for a given day, take a look at the Google Drive folder below. You can see individual dates to the top left of each image and also in the file name of each image.

See all 365 individual days here

Notes: in the Guardian's original csv, I found that the date formats were a bit messed up, so I fixed this and added in some new, corrected date fields to the right of the spreadsheet. I also added in individual columns for day and month. I'm not a gun campaigner, this was just an interesting dataset for me to use. If you have any questions, feel free to get in touch. This data covers homicides only, no suicides. I updated this post on 5 April 2017, to include cumulative totals in the maps. Updated again on 10 April 2017 to include a cumulative version. It looks a bit ugly at the end but then it's a pretty 'ugly' dataset. I thought this was another interesting way of displaying the data.

Sunday, 26 February 2017

Train Stations of Great Britain

In my ongoing quest to answer the burning questions of our times, I have decided to continue my data-based boffinry by looking at a couple of questions I sometimes think of when zipping up and down the country on the train. I'm sure I can't be the only one, so here are some results that I've had saved up for a while. The first question is, 'which parts of Great Britain are furthest from a train station'? The second is 'how many train stations are there in each local authority or parliamentary constituency?'. Yes, I know I need to get out more but if you're reading this you probably do too - so take a look at the first two maps below.

Not exactly earth shattering, but some interesting snippets

You can click on this to see a bit more detail

Not entirely unexpected patterns here. In part, I also did this to use as teaching material in the future (it uses a basic GIS operation) and I used 30km just because it produces an interesting result. You can see the area around Bude in North Cornwall is England's largest area without a station. This issue has been raised in parliament many times, including in 2014 by the previous MP for the area. The furthest areas from stations are all in the mostly sparsely populated north and west Highlands, but also in and about the Cairngorms and the Borders - though the latter has just got a lot smaller thanks to the re-opening of the Borders Railway. West Wales and a bit of North Wales is also not on the map in this regard. There is also a tiny sliver of land in Yorkshire that sits just outside this 30km buffer distance. Some zoomed in maps follow...

This is just on the Scotland-England border

Around Bude in North Cornwall (and a bit on Exmoor)

A zoomed in map of train station deserts in the Highlands

The Norfolk train-free zones

The West Wales no-rail-zone

Looking for trains in the Yorkshire Dales? Avoid this bit.

Okay, so having answered one burning question, let's briefly turn to the other. How many areas in Great Britain (and I'm just referring to the island of Great Britain) do not have a station? For Local Authorities, I make it 12 out of 376 and for Westminster Constituencies, I make it 49 out of 630. I've screenshotted the two files here but you can also explore them yourself in Google Drive

Many stations in the largest areas, obviously

Same as above - e.g. Highland coves a larger area than Wales

What should we conclude from this? Not much, but It's quite interesting to look at the local authorities or constituencies that do not have a train station - of which there are 2,557 listed in the Office of Rail and Road 2015-16 data that I used for this. The next two maps show where there are no stations - but there are possibly a couple of small inaccuracies (Kensington and Chelsea being one as three stations are right on the border there).

This is very interesting

If you've read this far, you should get out more

Okay, so that's about it. Some data notes below if anyone is interested. Also, the spreadsheets in the Google Drive folder have passenger entry and exit data - i.e. the headline 'passengers' figures that are used to identify the busiest stations - e.g. Waterloo with nearly 100 million in 2015-16. I have also added in average, max, min and sum figures on passengers for the aggregated local authority and parliamentary constituency numbers. Hours of fun.

Data notes: follow this link to get the 2015-16 data on stations that I used here - including the eastings and northings for station locations. I got the boundaries from the excellent ONS Geography Portal and they are, of course Crown Copyright (but also open data). As in, Contains OS data © Crown copyright and database right (2017). The data are compiled by Steer Davies Gleave on behalf of the Office of Rail and Road and they are accompanied by this interesting two page summary. In addition to the two spreadsheets, I have also uploaded the images in this post to the Google Drive folder. Train station vs railway station? I'm not bothered about this, or with data is/data are.

Tuesday, 21 February 2017

The UK's Best Place to Live

Where is the best place to live in the UK? The answer is simple. It's in my street. But that's just my view. Ask someone else and you'll get a different answer. Of course, this kind of thing doesn't really work when you do it from the perspective of individuals, and definitely not when you're trying to do it for the whole country, as I recently did for Outline Productions in their Channel 4 documentary, presented by Sarah Beeny. This blog post gives a little bit of the back story to it, discusses how mortified I am to be on the telly and a bit about the numbers. But how do we decide which is the best place to live in the UK? The real answer is that it depends upon who you ask and how you measure it.

I did the number crunching for this show

The background to this project is that I was contacted by Rachel Eadie of Outline Productions to see whether I could help them develop a 'best places' index based on a number of different criteria, such as income, house prices, wellbeing and so on. This was late in 2016 and I was a bit pushed for time, but it sounded interesting and I know the data pretty well so I said yes. After a few days of work and tweaking things I arrived at a final result. I received an initial 'wish list' of things to include from Outline Productions and I stuck to that where I could. The only criteria that I added was that I wanted this to cover the whole UK at local authority level - 391 in all - so that it could make some kind of sense across the entire UK. Too often these things only cover one or two parts of the UK. I included data on income, housing affordability, life satisfaction, happiness, jobs, unemployment, health, child poverty, and people aged between 20 and 29. 

The last bit highlights an important fact. We wanted this to be about the 'best place' to live for people in that age category. In this sense, think of it as a 'best place you might actually be able to move to and afford to rent or buy in' index. I say this because many existing 'best place to live' indices end up being topped by areas with an average house price of £500,000, and that's no use for most people. Also, given the propensity of people in our target age group to locate in larger cities, I also computed a 'proximity index' in relation to how close each local authority is to 13 major cities in the UK. Some places, such as Orkney, do really well on quality of life or 'best place' indices but their relatively low number of jobs and distance from major population centres means moving there is not a viable proposition many will consider - even if they are great places to live.

The Ring of Brodgar in Orkney (a great place to live) - source

What it was like to film this
I never thought doing television would be easy but by doing this little bit of work for a television production has made me realise a) how much goes into a single hour of television - so much work! and b) how bad I am at speaking, walking, thinking and communicating on camera. Seriously, I am not the most articulate person but I'm not completely terrible either. At least I didn't think I was. What I found is that having a camera on me made me robotic, incoherent and a lot more nervous than I expected. Things I know off by heart about data and places suddenly became impossible to recall when the camera was rolling. I also kind of forgot how to walk properly when being filmed, but I trust that the expert skill of the producer (Laura Mansfield of Outline Productions) means that I didn't end up totally ruining their programme. More seriously, it was an interesting experience and one that I think is useful. We filmed my bits in one day in Sheffield in December 2016, in ICOSS and across the way, outside The Diamond. I saw the final edit of the programme in January and despite not liking the look or sound of myself I thought the programme was well done. They sneakily got some good stuff in there about jobs-housing balance and the fact that indices are inherently subjective.

The numbers
I'm posting this just after the initial broadcast has finished in the UK (8pm, Tuesday 21 February 2017) so I can say a bit more about the final results now. It had been under embargo until that time. Remember that the areas I ranked relate to local authorities (e.g. London Boroughs, urban local authorities such as Leeds, Bristol and Newcastle, Glasgow, Cardiff, Belfast and so on). Individual places within local authorities, or places that go beyond the boundaries of individual local authorities are not part of the story here. It's based on the current 391 local authorities of the UK. South Ribble came top as our 'best place to live'. You may not have heard of it! But it's just to the south of Preston and includes within its borders places like Penwortham, Leyland and Bamber Bridge. 

Location of South Ribble - the UK's 'best place to live'

To add a bit of socio-economic data to this picture, you can look at the one of the maps from my Indices of Deprivation atlas (all other local authorities in England are here). In the map below, blue areas are among the least deprived in England, and the red and orange areas amongst the most deprived. You can see that for South Ribble most areas are in the least deprived deciles.

Deprivation map of South Ribble, from blue (least deprived) to red (most deprived)

Bear in mind that this all depends upon how you measure things - which of course also applies to just about anything in socio-economic studies. But, having said that, my follow up discussions with people who actually live there gave some more weight to the findings and there does seem to be a real dynamism in the area, possibly also because it is included in the new City Deal in Lancashire. I always try to 'sense check' the results of any data analysis against personal experiences of people who know areas, just to get an idea of whether the data seem to be telling the truth, as it were. For more on what's happening in South Ribble, see this piece in the Lancashire Evening Post. Remember also that part of the reason South Ribble came out top is because of what's nearby - and this is important to people when it comes to transport and jobs.

Next on the list was Warrington, located in between the urban local authorities of Liverpool and Manchester in the North West of England. This is a very good example of how transport connections, proximity to major urban labour markets and relatively affordable house prices combine to make it the kind of place that people could realistically move to and live in at the life stage which was the focus of the programme. Again, from personal experience I know that many people choose to live there for the reasons outlined above, so I wasn't very surprised by it. 

Motorways, railways, cities nearby - it's Warrington 

The North West of England dominated the top ten, but Blaby snuck in to the top 3. Blaby is another one of those places that is not on people's mental maps because it's the name of a local authority area rather than a well known town or city. However, it's a suburban local authority to the South West of Leicester in the East Midlands, as you can see below. You can see that, like South Ribble and Warrington, it is also very well connected in relation to transport (e.g. the M1) but this area also abuts a major English city - Leicester. This was a feature of several local authorities that came towards the top of the rankings. Other places like it include Rugby (at number 7) as you can see below.

Blaby - you might not have heard of it, but it's at number 2

Here's a basic map of the rest of the top 10 - just to give you an idea of the distribution of places. As you can see, 7 out of the 10 are in the North West of England - this is driven partly by relative affordability but also by things like happiness and wellbeing, and connectivity. Below this, you'll see a list of the top 25 places on the index.

The UK's 'best places' - top ten

An interesting mix of places in the top 25

Anyway, that's a little bit more information than is in the TV show itself so hopefully some people will find this informative. The precise position of places on the list does, as I explained before, depend upon how you choose to weight and measure individual indicators but this is how things came out. If we repeat it - e.g. in Best Places 2020 - we might find that different places come out top. The fact is that anywhere in the UK could be someone's own 'best place to live' with the exception, I suppose, of prison! Our programme gives the 'best place' notion a slightly different take on things. 

Notes: in the bits when I discuss the data, there are a few times when I've described it in ways that may seem unconventional - or even wrong. One such example is in relation to disposable income when in fact what I'm really discussing is discretionary income. I wanted to try to be informative without being too technical but at times I may have gone a little too far and simplified things more than was necessary. Having said that, I realise that the kind of people I hang around with might know these terms but the average TV viewer probably doesn't know or care. I mention it here in case anyone spotted this or any of the other things that seem a bit odd. After all, this is part statistical exercise and part entertainment. And why am I getting involved in this stuff anyway? Well, I like to do interesting work beyond the confines of the academic world and this seemed like an interesting opportunity to offer a different take on 'best places'.