Far too many words about a chart

This is a bit of a long read, because I wanted to put down my thoughts on a chart I made that has been quite popular and quite widely shared. See below for one version of this chart - it ranks all 650 UK Westminster parliamentary constituencies by deprivation and colours them by which party won the seat at the 2019 General Election. You can skip straight to the data and charts folder now if you want, but if you do, it's probably best if you also read the notes at the bottom of the page. Because this is a chart made of blocks, or bricks, and because of recent 'red wall'/'blue wall' media talk I'm going to call this a wallchart, but you can call it whatever you like. If you do like reading about charts and dataviz and the thinking behind them, read on. I cover all the small things.

Click to see it full size

Background

I will not go on too much about the backstory, but I'm here to tell you that there have been quite a few votes on things across the UK in recent years. This is something you may have noticed, particularly if you were up all night watching, waiting, or commiserating at any recent election. Because of all the votes there have been lots of maps, lots of data and lots of news. Some good stuff, some confusing stuff and probably just too much stuff. But anyway, after the 2017 UK General Election in June 2017 I wrote a piece on here with some maps and charts. Almost as an afterthought, I said to myself 'I wonder what it would look like if you took all 533 English constituencies, turned them into blocks, ranked them by deprivation, and then coloured them by the party that won the seat'. Then I thought, 'how quickly can I do this?' So, I did it very quickly and messily in Excel - see below for what the first one looked like and a screenshot from the original Excel file.

What's 533 divided by 10?

Some immediate observations about this chart that came to mind at the time: i) it's annoying that there are 533 rather than 530 constituencies in England - neat charts > representative democracy; ii) this chart tells us something that is blindingly obvious but even so it's also strangely striking; iii) 'hey, what's that blue block doing on the left hand side'? (it's Walsall North); iv) I didn't realise the orange blocks would nearly all be on the right half of the chart; v) I need to figure out how to deal with the fact that 533 doesn't divide by 10 neatly and maybe make the top of the chart nice and flat.

Excel?! #$@&%*! Excel!!?? Say it ain't so

Next versions

I really didn't think much of it at all after I hit Publish and never planned to come back to it, but at some point in the year or two that followed, Danny Dorling got in touch with me about it using it and then I did new versions for different years, including ones for the UK that were much neater because of the fact that there are currently 650 constituencies and this divides neatly into 10 columns of 65. I also changed the colours, added more labels and some versions had constituency names as well. The initial one used deprivation data for English constituencies produced by the always-excellent team at the House of Commons Library. For the earlier UK versions I used UK-wide deprivation data produced by Abel et al. (2016) to calculate the ranks. 

For most versions of the chart, including the UK ones, I've had to calculate constituency-level deprivation myself using a population-weighted method, aggregating up from small areas (LSOAs in England and Wales, data zones in Scotland and SOAs in Northern Ireland). A big hat-tip here must go to my collaborator and all-round political data boffin Philip Brown for prompting me to come back to this and do new versions. When the 2019 General Election came round I decided to dust off the old files and make a version that looked much nicer. The reason I've come back to this again now is because Alex Parsons recently published a new set of UK-wide deprivation data, based on the earlier work by Abel et al. 

The left hand column is turning blue

This was the 2017 General Election edition

It took a while to put the data together for the different years and although I would have loved to put together one for earlier years - particularly 1992 and 1997 - it would have taken me too long to do it.

Read on below for more on the design stuff

Since I did the first one of these in 2017, other people and organisations have done their own versions using the same concept. I am not sure if it was done before that, but I haven't seen one like it. All I do know is that I got the original idea from a chart Owen Boswarva made in relation to age and party colour. 

Here's a version that the Economist created (below) - horizontal rather than vertical and much more elegantly executed. It was in their 'Who are the Conservatives’ new voters in the north?' piece from December 2019 - which I took a picture of in the print edition just because it's nice to see things in print. I had to travel a further from home than I expected to actually find a print copy of the Economist because none of the local newsagents near me in Sheffield Brightside and Hillsborough (left-most column) stock it, but that's another story. 

This is a good place to mention the ecological fallacy, because I am certainly not claiming I am poor, or deprived - this is just a nod to the fact that internal variation obviously exists in all areas, even if the overall ranking is a good representation of the overall pattern across the UK in terms of relative deprivation by area.

Lovely!

Call me old fashioned but I do like print

There are also other versions out there, including a remain/leave by deprivation one that Helen De Cruz did - copied below. I thought this one was really interesting, particularly column 10.

Column 10 is clearly the most remainy

There was also a version of the chart looking at age and deprivation - this was done by the Resolution Foundation in December 2019 and can be found in this twitter thread (I've pasted the chart below as well). I decided to do an age one in my most recent iteration - see below for that.

Very nicely done

I have also created a dark mode version of the latest chart from 2019 and a chart ranked by median age as well, so I'll put these ones below too. You can see this in my more recent twitter thread about it.

Turn the lights off!

Age rank gives us a similar pattern

Design

The legendary Andy Kirk very kindly noticed one of the things I did in earlier versions of the chart so click that link for a bit of independent insight on one of the details. Here I will set out a few reasons why I think this chart connects with people - and why it also makes sense to me. One of the main reasons is that it builds upon some things that are hard-wired into our brains already.

• Rank: ranking things is not always a sensible idea and it's always somewhat fuzzy. Things get collapsed into categories made by people and this can mask internal variation. However, even if ranks can be quite a coarse measure, the constituency ranks by deprivation are more or less what we might expect for England or the UK as a whole. The places with the most 'poor' or 'deprived' neighbourhoods are on the left of the chart, with the wealthiest areas on the right. Also, and fundamentally, ranks are easy to understand, so this is not much cognitive work for the reader, once they know how the chart is laid out with the most deprived areas at the top left and least deprived at the top right - and this requires only minimal labelling. 
• Colour: this one is of course more context dependent in that UK readers (or those who follow UK politics) don't need more than a split second to interpret the colours, without a key. We see red, we see Labour, we see blue we see Conservative, we see yellow, we see SNP, and so on. There aren't many occasions when we can do this with colours but political charts and maps are one example where we can. The cognitive load here is again quite low, I would argue. I will just add that I've tweaked the original colours because the red/blue combo was blinding in the first chart. My colours are a mix of commonly used ones (e.g. on BBC election coverage) plus a bit of artistic licence. 
• Position: The general idea is for this chart to be more about the forest than the individual trees. Having said that, being able to position individual areas on the spectrum from more deprived (left) to less deprived (right) is very important. It also matters symbolically that the red areas are on the left, and blue on the right because this is a visual match for the traditional left/right political divide. Again, we can argue with definitions and concepts here but having Labour on the left and the Conservatives on the right matches what is already hard-coded in our brains if we follow politics even just a bit.
• All the small things: there are lots of little things I've tried to do with the different versions of this chart to make it easier to digest, but without overwhelming the reader. One example is the faded axis labels on the left and right of the chart (below). I did this because I only really wanted people to be able to understand how the ranking worked, but without introducing any cognitive overload. I also don't want to put too much stock in individual rankings - I'd rather people saw the groups of 10 (labelled across the top) as more indicative of relative socio-economic status. That's also why the label colour is lighter for the individual blocks. The number fade thing was something I got from John Burn-Murdoch on twitter but I can't find the original post on that. So, the faded axis numbers are very much there so readers can get what's going on, but without it being too much of a focal point or distraction. 
• Notes, etc: with this being the internet, there are small dangers associated with doing this kind of thing. Quite often people take things without attribution or context and share them with friends and followers as their own (even academics who ought to know better). People also see what they want to see - particularly with politics and causal links on a chart like this. The idea behind the chart was simply to see what the pattern looked like. I'm not trying to identify causal links here, but clearly people will do this anyway. Looking for causal links is a different kind of statistical activity, but this chart raises such questions with people and that's to be expected. Anyway, in order to avoid some of the problems with no-context sharing, I try to put enough text on the chart to explain a) what it is; b) how it was made and c) data sources. I usually add my name as well so that if someone wants to find out who did it they can get in touch - this would be more complex if my name was John Smith - but thankfully there aren't that many Alasdair Raes doing weird colour charts these days. Still, people will crop your name off because this is the internet.

There are plenty of things I could improve about the chart- and I've tried to keep doing that - but at the same time this wasn't part of my day job (work sucks, I know) and it wasn't for any particular project so I can't invest too much time in it (he writes, whilst writing War and Peace instead of finishing the presentation for next Tuesday). Having said that, there are a few final points I'd make on all the design-related stuff.

1. Doing this in Excel might be okay for a quick-and-dirty experiment but it's clunky and messy and every time I go back to the original I am confused. It also takes me ages to find the original files. Having said that, I'm not bothered about what tool I use to do a job so long as it works - and there is nothing wrong with Excel, used properly. I like to think I follow 'the law of the job' rather than 'the law of the instrument' but even so this was a messy mess first go round. It started off as a bit of Excel formula, pivot chart, conditional formatting and a few hacks and it worked, and that's okay.
2. After Excel I normally used GIMP to add some text and other bits. Again, fine but not optimal. I think in previous quick versions I even drew some arrows in PowerPoint and I'm not even sorry because it worked fine and served a purpose.
3. I don't really like to label the individual blocks because the chart idea wasn't about that, plus some constituency names are very long and the font size has to be small. But I do like to think about user needs so some versions have them because people always ask. There's also a searchable html version so you're only a quick browser search away from finding your area.
4. Most importantly, the way I have done this to date is pretty sub-optimal to say the least but then sometimes sub-optimal things happen and you get locked in to them. 

So, I thought to myself... why don't you try to make this less bad and also sharable? Good idea. But how? Read on to find out and grab the data for yourself.

Wallcharts for everyone

I normally work with spatial data but the chart I'm talking about here is non-spatial - it's just a series of ordered blocks and where they sit in the chart bears no relationship to their geographic location in the real world. Nonetheless, what I decided to do was keep it simple and create a 10x65 GeoPackage that covered roughly the same areas as the UK and with the constituency deprivation rank number assigned to each block - see below for what this looks like with the standard UK election map on top of it. Once I'd created the 10x65 grid I prepared a separate spreadsheet with all my other data in it - including age, ethnicity and so on. I then joined this to the 10x65 grid to make the final GeoPackage.

The 10x65 GeoPackage can be found in this web folder - I've also shared it in different formats as in the hope that it can be more widely used (shp & geojson). When you add it to QGIS it will be coloured according to the party that won each seat in 2019. I do realise that for non-GIS users this might not be ideal, but you can easily take one of the existing versions and convert to a more suitable format for your own needs. There's also a qml file in the folder that you can use to style the layer, as well as one svg that I extracted as a test.

This is simple, but works well

Just in case you download the UK 650 block file and are confused by the column headings, here they are:

• fid - just a numeric identifier, which can be ignored
• DEPRANK - the rank of each constituency based on Parsons' 2021 UK small area deprivation data, aggregated up to constituency level (by me)
• CODE - ONS constituency code
• CONSTIT - constituency name, upper case
• NAME2019 - constituency name
• COUNTY - the county a constituency is in
• CTRY_REG - the UK country or English region a constituency is in
• COUNTRY - the UK country a constituency is in
• GE2010, GE2015, GE2017, GE2019 - the part that won in each of these years
• AGE2019 - the median age of each constituency in 2019
• AGERANK the age rank of each constituency in 2019 (1 is youngest)
• POP2019 - the population of each constituency
• MINDECILE - the minimum UK-wide decile any single LSOA/DZ/SOA within a constituency is in (1 being the most deprived)
• MAXDECILE - the maximum UK-wide decile any single LSOA/DZ/SOA is in (10 being the least deprived)
• AVRANK - the average rank of all LSOA/DZ/SOAs in a constituency - where 1 is most deprived and 42,619 is least deprived (so, e.g. an average rank of 4262 would mean that the average area was among the 10% most deprived nationally)
• POP2011 - the population in 2011 (relates to the indicators below)
• WHITE2011, MIXED2011, ASIAN2011, BLACK2011, OTHER2011 - the number of people in each ethnicity group from the 2011 Census (data published by the House of Commons Library)
• WHITEPCT, MIXEDPCT, ASIANPCT, BLACKPCT, OTHERPCT - same as above, but % rather than total number
• WHITERANK, MIXEDRANK, ASIANRANK, BLACKRANK, OTHERRANK - this ranks all 650 constituencies according to ethnicity, so that an area with a WHITERANK of 1 has the highest % of white population (as of 2011, we don't yet have 2021 Census data, so keep that in mind) and a WHITERANK of 650 has the lowest % of white population - and so on for the other groups.

I'm almost done here, but before I wrap up here are some different versions of the chart - now much easier to create having done the hard work of putting it together in a more suitable format. All I need to do is apply a simple filter and I can get all sorts of interesting charts out of it.

The overall 2019 chart

2019, just for England

2019, just for Scotland

Coloured using 2010 results

Constits that are 25% + non-white

The 100 oldest constituencies

Lab in 2017 but Con in 2019

What colour the 2010 Lib Dem seats are now

I've put the high resolution (300dpi) versions of these charts in the new wallchart folder I created for this project, so feel free to use them as you wish. 

Well, this was quite a long post so if you read all of it then please accept my thanks and congratulations.

This was mainly about all the small things, so if you noticed the Blink 182 song references, well done - carry me home! 

Notes

The deprivation ranking is somewhat England-centric. You can read more about the new UK deprivation data I used for the new charts on Alex Parsons' repo for the project. This new iteration is all possible because of the great work Alex did. If you want to follow the method then you should definitely read his notes on this - as well as the bit on mapping (which I provided some of the text for). There are four different files you could use to create a UK-wide constituency ranking. I used the UK_IMD_E.csv file, but the overall pattern is not likely to change much when you use the others. I also used the most recent mid-year population estimates (2019) for weighting.

The individual ranks of constituencies are likely to move slightly up and down if you use a different method but, on the whole, I am confident they are in the right place. 

Feel free to use, re-use, improve or otherwise adapt this - I know there are many, many people with better skills and ideas than me so this is very much a starting point.

If I spot any typos or other errors I'll back later to clean them up.

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.

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.

How Urban is Deprivation in England?

The answer to the question above is 'very'. However, it's not all about the big cities, and there are significant pockets of deprivation in rural areas that don't often get the attention they deserve. In this post I take a little look at the distribution of England's most deprived areas (the 20%) in relation to the 2011 Rural-Urban Classification - developed for the government by Paul Brindley and Peter Bibby here at Sheffield. The ideas is to try to shed more light on the kinds of places that we find among England's 'most deprived'. Let's take a look at this in overview first of all.

This maps shows the 20% most deprived, with rural-urban area types

Sometimes it's a bit difficult to tell from a map like this how it all breaks down by category, so I also did two sets of charts - one each for the % and total in each deprivation decile in England (where decile 1 is most deprived, 10 least deprived). In the % charts below (click to enlarge) you can see the nature of the distribution by rural-urban classification type, followed by the total number in each decile in the second set of charts.

Green for rural types, red for urban types here

By absolute numbers, the urban areas dominate

Given the nature of what is being measured, and the distribution of the population by socio-economic status, this is not particularly surprising. What I do find interesting is the extent of deprivation among non-'conurbation' areas (one of the types in the rural-urban classification). You can see these in the map below, coloured blue, followed by a zoomed-in map for Blackpool.

These areas seem to get less press than the more famous examples

All these areas in Blackpool are among England's 20% most deprived

In some areas there is more of a mix of area types on the rural-urban classification, with rural, town and conurbation areas all featuring. Two examples here are County Durham and Barnsley, as you can see in the maps below (green for rural, blue for town, red for conurbation). This reflects the industrial heritage of these places but it also raises the issue that the policy solutions (or responses) may need to be tailored to meet the needs of very different local contexts. Well, that's for another day but at least this typology helps us identify the different character of areas that are often lumped together.

It's easy to spot the coalfield areas here

Barnsley also has a mix of areas

One area of the country that often seems to be overlooked - at least from a national perspective - is Cornwall. It's a very large local authority and the patterns of deprivation here are much more dispersed and not in conurbations. So, I've extracted a couple of maps here too - one with place names and one without.

Significant areas of deprivation here, but less obvious on a map

This time with labels, for anyone not from Cornwall...

To highlight one further kind of deprivation in England I have also produced a map of East Lindsey, which helps identify some areas of deprivation on the east coast. As you can see, they are a mix of rural (green) and town in the rural-urban classification.

East Lindsey is the 5th largest local authority in England

So, the answer to the question is of course that deprivation in England is 'very urban' but also that we find pockets of deprivation all across the country and often not in the kinds of inner-city locations we hear most about in the news, in government reports and in my maps. So, I'm trying to draw a bit more attention to it with this post.

Finally, here's all the areas together in a single animated gif, just to highlight the variable geography of the most deprived 20% by area type.

If you're looking for a map of deprivation in your area, see my IMD 2015 resources page.

Children living in deprived households in England

Not a particularly upbeat post title today, but it's an important topic too often overlooked. I wanted to shed some light on the matter because there are copious amounts of data on the issue, including those released as part of the 2015 English Indices of Deprivation, which I've explored in-depth in the  past though a series of maps. A recent Twitter message from freelance writer and HuffPost blogger Shumailla Dar prompted me to re-visit this topic (thanks) and since I had most of the data set up, I thought I'd do another map series - this time with the 'Income Deprivation Affecting Children Index' (IDACI) from 2015. See below for an example of what this looks like. In all maps, I've added a little inset to show the pattern from the overall Indices of Deprivation 2015, for comparison. I also show the percentage of each local authority's small areas in each decile on the IDACI measure nationally.

Quite a north-south divide in relation to income deprivation and children

Okay, so what is 'income deprivation affecting children'? It's the proportion of all children aged 0 to 15 living in 'income deprived' families. 'Income deprived' is defined as 'families that either receive Income Support or income-based Jobseekers Allowance or income-based Employment and Support Allowance or Pension Credit (Guarantee) or families not in receipt of these benefits but in receipt of Working Tax Credit or Child Tax Credit with an equivalised income (excluding housing benefit) below 60 per cent of the national median before housing costs'. Again, not the most exhilarating topic, but given the impact this can have on young people's lives, it's such an important one. If you want more information on the details, see the Indices of Deprivation Technical Report. Beyond the technicalities, here's how it looks on the ground in Middlesbrough, the local authority with the second highest proportion of children living in income deprived households (35.7% overall).

Second only to Tower Hamlets on this index

The Indices of Deprivation 2015 Research Report found, though a user survey, that whilst 99% of respondents had used the Index of Multiple Deprivation, the figure for IDACI was 69%. Still high, but it suggests a lot of people haven't looked at it, particularly since most respondents were people already working with the data in local authorities, universities, central government and charities. Most 'normal' people have very little idea that the data exists or what the patterns are like in their area - hence today's post. There is also a similar index which reports income deprivation affecting older people, but that's one for another day. Before showing any more maps, here's the top 20 local authorities across England in terms of the percentage of children living in income deprived households.

Source: DCLG, 2015, p. 23

Now for some more maps, before I provide a link to the folder with a map of every local authority in England. First of all, here's Tower Hamlets. Remember that the bars show the percentage of small areas (LSOAs) in each local authority that are ranked in each decile within England - which sounds a bit confusing, I admit. To give an example instead - for the map below, 54.2% of Tower Hamlets LSOAs are ranked within the most deprived decile in England on the IDACI measure. Just bear in mind that the maps present a relative picture for England as a whole and that, broadly speaking, red = bad and blue = good.

This issue is very well know, but persistent, in Tower Hamlets

Chiltern's at the other end of the scale - but note the single red area

A real mix of areas in Bury

Liverpool is a city of contrasts on this measure

What does any of this tell us? Of course, we know that some places are rich and some are poor and that this will inevitably have an impact upon children in those areas but these maps reveal nothing of cause and effect. Rather, I hope they will provide local agencies, analysts and residents with an opportunity to explore patterns related to income deprivation affecting children in their area and perhaps to think about a topic they hadn't before. It's certainly not a new issue but one that, I think, we could do a lot more about tackling. But that's a step too far for today because I just wanted to share these maps after running off a new batch after being prompted to think about it. 

Click here to go to the big Google Drive folder with all the maps

Files are ordered alphabetically, by local authority name

If you want to see comparable maps for the 2015 Indices of Deprivation overall, the 20% most and least deprived and other varieties of deprivation map, see my main IMD 2015 page. If you find any of these maps useful, feel free to use them and share them. 

Notes: you can find full details of the data and method on the government's English Indices of Deprivation 2015 web page, including the IDACI spreadsheet. I'm not a member of, or affiliated with, any political party in case anyone asks about the red and blue colour scheme! The labelling is a bit wonky in places owing to the variable coverage of OSM data. I did a version with Ordnance Survey labelling too but this had too many points, but hopefully some of the labels help identify key areas you are interested in.

Arctic sea ice, 1978-2015

During the recent Paris climate talks, I began to think more about the topic of arctic ice melt because I really didn't know too much about it, beyond the general knowledge that it's disappearing fast. So, naturally, I decided to see what data I could get my hands on to explore it further and understand it better. I discovered that the 2015 Arctic sea ice minimum was the fourth lowest ever and that NASA's Goddard Media Center have produced some amazing visuals on it. More excitingly for me, the National Snow and Ice Data Center (NSIDC) publish richly detailed data and images on sea ice, going back to 1978. This seemed like a good topic for my first post on the new blog. So, to kick off, here's the extent of Arctic sea ice in November 1978, the first month covered by the dataset (click any of the images to enlarge).

The first month for which data are available

The NSIDC do have their own image animation tool which is derived from monthly images, but I wanted to do my own versions which include only images from September and March - the months of minimum and maximum ice extent respectively. If at this point you're wondering how the data are derived or other questions, check out the FAQ section on the NSIDC website. If you're looking for more on sea ice in particular, check out the NSIDC sea ice news section on their website - it's a great resource. Also, to provide a little context, Arctic sea ice extent in November 2015 was just over 10 million square km - just larger than Canada. It normally peaks in mid-March at about 15 million square km and is at its minimum in September - with a mean of about 6 million square km (twice the size of India). However, you'll see in the animation below that it bottomed out at only 3.4 million square km in September 2012. 

Every September from 1979 to 2015 (animated)

I've also pulled out the static image from September 2012 as it's easier to see this way. If you're planning to go all the way from the Atlantic to the Pacific in your container ship, be careful - it's not plain sailing yet, as this Washington Post story notes well.

Arctic sea ice, record seasonal minimum in 2012

It's also really interesting to look at the month when sea ice is at its maximum extent. In March each year the ice is more than double the area compared to September, though there has been some significant variability in the minimum lately. You can see the extent of Arctic sea ice for every march from 1979 to 2015 in the animated GIF below.

Every March from 1979 to 2015

The data are a great resource for looking at trends, but of course not everyone is interested in trends. For example, the Daily Mail ran a story on this kind of thing in 2013, noting the growth in ice cover between August 2012 and 2013. It also grew in extent between March 2011 and March 2012 (see below) but of course this isn't consistent with the long-term trend of sea ice loss documented by NSIDC. The overall story is one of ice melt.

March 2011 maximum was 14.6m sq km / 5.7m sq miles

March 2011 maximum was 15.2m sq km / 5.9m sq miles

There are so many powerful visuals on this topic, including NASA's own great small multiple showing all months from 1979 to 2014 and a recent Goddard video on the 2015 minimum. One thing I've seen less of is a single year in a simple animation, so that's what I've done below for 2014 in another animated GIF. 

Full year animation for 2014

That's all for now. I'll be returning to my normal urban data topics in future but I thought I'd kick off the new blog by doing something a little different and learning something new. In the spirit of the title of the blog (see the About section for more on that), I wanted the first post to have some 'stats, maps and pix', and this seemed like a good fit - plus I also learned something new about a phenomenon which has occurred entirely during my lifetime. Finally, here's the September and March animations again, but this time at half the speed.

September ice extent - at 1 frame per second

March ice extent - at 1 frame per second

Data: I used data from the National Snow and Ice Data Center, which is a fantastic resource. I've barely scratched the surface of it but I now have a much better understanding of what's going on thanks to their work. Here's a direct link to the FTP directory containing the Shapefiles. The NSIDC is based at the University of Colorado in Boulder and is affiliated with the US National Oceanic and Atmospheric Administration.

Software: I used QGIS 2.10 for the mapping and GIMP to create the animated GIFs.

Citation: Fetterer, F., K. Knowles, W. Meier, and M. Savoie. 2002, updated daily. Sea Ice Index. Boulder, Colorado USA: National Snow and Ice Data Center. http://dx.doi.org/10.7265/N5QJ7F7W.