Thursday 20 December 2012

Google Fusion Tables Info Windows

A short post today related to info windows for Google Fusion Tables maps. In case you didn't know, an info window is what pops up when you click on an area of a Google map - as shown below. I've blogged about this in the past and today am sharing some more tips in relation to how to get a Google Street View snapshot of an area to appear in the info window. I just added this in to my new SIMD website.

This page tells you how to add a Street View snapshot and there isn't really much to it. You just need to make sure you have an API console key (baffled? see this for how to get one) which is just a key code that you need to make it work.  All you need to get one is a Google account. The other thing you'll need is two columns in your table specifying a latitude and longitude. I do this in ArcGIS before importing the data into Fusion Tables. Once you've done this you can add in the Street View code to the Fusion Tables info window layout (via Tools, Change info window layout... as below) and then add in the code via the Custom tab.

I've added in an Employment page to my new SIMD site where this is all put into practice - I have an info window chart, some data and a Google Street View snapshot in it, plus some text. Here's the code that makes it work - I'm certainly not a coding expert so apologies if it is a bit messy. If you're into this kind of thing, I hope you find it useful. 

You can change the dimensions from 400x400 to whatever you like. The curly brackets specify which column the lat/long comes from so you will need to make sure that this matches the name of your lat/long columns.

The only downside to this is that the image it displays is fairly random and not always representative of the area people click on. Sometimes, no image is available.

Tuesday 18 December 2012

SIMD2012 - An Interactive Website

With the release of the latest version of the Scottish Index of Multiple Deprivation I thought I would take the time to put together an interactive mapping website so that people who are interested in exploring spatial patterns of deprivation could easily interact with the data. The official Scottish Government interactive mapping site has some nice features but I find it a bit cumbersome and the map interface is too small for my liking so that's why I've produced my own version, based on Google Fusion Tables.

Putting this together has prompted me to develop some additional mapping tools using Fusion Tables code and these can be accessed via the 'Tools' tab on the new website. The 'search and zoom' allows you to enter a place you want to look for and when you hit 'Search' the map immediately pans and zooms to that location.   The other tool I've created simply lets you turn the SIMD map layer on and off, which is quite a useful feature.

I've just looked at the relative ranks of places within Scotland in this site. For details of absolute change you can see the employment and income domain data available from the new SIMD 2012 website.

Monday 26 November 2012

Population growth in Manchester city centre, 2001 to 2011

Manchester city centre experienced a population explosion between 2001 and 2011. In the area covered by the map below the population increased by nearly 400% - from 5,957 to 23,295; a rise of 17,388 people.  In 1991, the same area (roughly) had a population of 2,887, as you can see here. Anyone who knows Manchester will know about this but possibly not about the scale of the change. A lot has been written about these changes (e.g. Centre for Cities in 2006, Manchester Evening News in 2012) but it is clear that much of the growth experienced in Manchester has been driven by a new phase of city centre living. Click an area on the map to find out more about it and zoom in to the larger version of the Google map to see it in more detail.

But who are these new residents? Chris Allen, writing in 2006, said they might be a mix of 'counter-culturalists' from the new middle class, 'city centre tourists' from the service class and 'successful agers', who tend to be over 50. Whoever they are, they're living in the new apartment buildings that began to sprout in the perforated urban landscape of central Manchester over a decade ago.

The map above contains a total of 15 small areas known as super output areas. If you click on any area you can find out how many people now live in each. The map also tells you the total land area of each small parcel and how many households are in it. You can find more of this kind of information via the Guardian datablog and also by clicking through to the raw data on the ONS web pages. It was only released on Friday but I've been looking at it now because I'm writing a short paper on small area population change in the English core cities. Central Manchester has grown the most but other cities have also experienced rapid expansions.

A note for spatial analysts: the 15 LSOAs you see in the map above are new. They are just 15 of the 229 new LSOAs across the core cities and this makes change over time analysis a little more tricky. Manchester's total population growth during this period was just under 20% (or 80,000) so the growth in the centre accounted for just over 21% of the increase.

Friday 23 November 2012

Premier League Poverty?

The financial health of the English Premier League is a regular topic of discussion. No matter how you look at it, the sums of money involved in the operation of Premier League teams are immense and it looks even more striking when you consider the locations of their stadia - typically in the most deprived areas of England. This is related to a number of factors (e.g. history, land values), but in my work on neighbourhood deprivation and mapping I've noticed a common trend over the years so I decided to look at the location of all 20 current Premier League teams in England and how deprived their local area is, using the Government's official measure of deprivation (the IMD*). The maps below show each team and its area's deprivation rank within England, where 1 = most deprived and 32,482 = least deprived. Shading: red = poorest 20% of areas in England, yellow = next poorest 20%, and so on... Liverpool is most deprived and Fulham least. See below for more.

Arsenal - IMD 4,432 
Aston Villa - IMD 479

Chelsea - IMD 5,483

Everton - 1,070

Fulham - IMD 19,076

Liverpool - IMD 219

Manchester City - IMD 599

Manchester United - IMD 10,235

Newcastle United - 18,570/6,582 (split)

Norwich - IMD 12,253

Reading - IMD 6,843

Southampton - IMD 855

Spurs - IMD 950

Stoke - IMD 2,171

Sunderland - IMD 6,291

Swansea - WIMD 374

West Brom - IMD 1,619

West Ham - IMD 3,593

Wigan - IMD 429

QPR - IMD 7,848

In Newcastle, the stadium is split between two areas, so I have given the deprivation ranks for both areas. The English Indices of Deprivation obviously only cover England so I have included a map for Swansea which uses the Welsh Index of Multiple Deprivation figure from 2011. More on that in a previous post. Similar patterns exist in Scotland, as you can see from the bottom part of this 2009 post. What does this tell us that we didn't already know? Not much, but it does provide some hard data and an overview of the contrast between the wealth of clubs and the poverty of many local areas they are located in.

* The English Indices of Deprivation 2010 are used here. They rank each of the 32,482 Lower Layer Super Output Areas of England from most deprived (a rank of 1) to least deprived (a rank of 32,482), taking into account such things as income, employment, education and health. There was a similar piece of analysis in Regeneration and Renewal in 2009 but this looked at the local authority areas only and not the specific location of stadia.

Sunday 18 November 2012

US Election 2012 County-Level Results

I've been experimenting with the 2012 US election results at the county level* published on the Guardian Datablog and comparing percentages for Obama and Romney. One of the most striking things is how Obama won in DC (over 91.4% of the vote) and in the Bronx, NY (91.2%). The highest percentage for Romney was in King County, Texas where he won 95.9% of the vote. The image below shows these patterns and also includes some information on race, with % Hispanic and % African-American mirroring, to a large extent, the percentages of voters choosing Obama. It's not really that simple of course, but there is a correlation. One interesting nugget here is the difference in total votes won in counties with the highest voting percentage for each candidate. In the 5 counties with the highest Obama percentage, almost 850,000 voted for Obama. By contrast, in Romney's top 5 counties the total was just under 20,000 voting for him.

What does any of this mean? It means that the Republican Party probably need to think about how to do better in cities, with Hispanics and with African-Americans, but they already know that. Romney was very successful in areas where not many people live but not successful enough in major cities. It's all pretty obvious but it stands out more when you look at it on a 3D map!

*(for the continental United States, so no Alaska or Hawaii for now)

Monday 12 November 2012

Shannon County, South Dakota: Democrat Stronghold

I've talked about 3D mapping here before, and why I like it, so I thought it was about time for another one. Since the US presidential elections have just taken place I thought I'd look at some of the data and make some maps. I'll post some more when I have time but for now I thought this 3D map showing the ratio of Obama to Romney voters at the county level was pretty interesting, not least because it identifies an interesting high point in South Dakota. 

Click here for a full screen version

South Dakota is a Republican stronghold but Shannon County is the exact opposite. In both 2004 and 2008 it had the highest Democratic voting percentage in the United States (over 85% both times) and in 2012 more than 93% voted for Obama. This might not come as a surprise when you discover that Shannon County is located entirely within the Pine Ridge Indian Reservation and that out of around 8,700 registered voters, 6,500 are registered Democrats. 

One advantage of using a 3D choropleth is that you can often differentiate between places within the same class in a way that is impossible with a normal 2D choropleth. It usually makes it very easy to see places that stand about - such as Shannon County - and it is a powerful way to visualise this kind of data. There are down-sides too (visual occlusion being one of them) but the continental United States is a nice shape with nicely divided counties, so it works well there.

Beyond Shannon County, the Obama strongholds with real weight in terms of population are DC and the Bronx. In both cases Obama voters outnumbered Romney voters by more than 10 to 1 - 222,332 to 17,337 in the case of DC and 264,568 to 24,430 in the Bronx.

More on this kind of thing coming in the near future...

Wednesday 31 October 2012

ESRC Success Rates 2011/12

I'm currently in the process of thinking about submitting another ESRC grant proposal. To date, I have put in a few but without success. I'm not easily discouraged though and I have had a lot of good feedback (but no money!). Thankfully, I have been able to secure funding from other sources but the vital statistics from ESRC do suggest that I might need to up my game considerably - or switch to Socio-Legal Studies! What am I talking about? Success rates of course. The LSE Impact blog just did a post on this issue and the figures really are striking. So striking, in fact, that I felt the need to turn it into a chart. Just look at the data (click charts below). 39% success rate for Socio-Legal Studies and 0% success rate for Environmental Planning (among others).

Click chart to enlarge

Click chart to enlarge

In relation to the number of submitted grants, Psychology is the clear category leader with 185, though the success rate in this category is just slightly higher than the average (16% compared to 14% average). The highest number of grants to any one category is in Sociology, with 16 awards (19% success rate) during the 2011-12 period.

See the full ESRC report here, where you can also find a break down of the figures by institution.

Wednesday 24 October 2012

Digital democracy in action

A short story about digital democracy today. Anyone with an interest in politics, democracy or government will have been intrigued by the publication of the latest proposed parliamentary constituency boundaries for England. The next step would then obviously be to look more closely at the proposed changes, but how might we do this? Well, you can go to the website and look at some Excel files or some PDFs. Or, you could go to a public office and look at a map (I even took a picture of one of these - below).

The problem here of course is that it is 2012 and most people might like to have a nice interactive, user-friendly map to look at which allows them to zoom into their area to see how the changes affect them. But this wasn't produced by the Boundary Commission for England. Instead - just like last time - a user-friendly interactive map has been produced by expert-enthusiasts, if I might call them that. The one I did was just a quick overnight project and although the new version is much nicer than my one it was still not produced by the people who actually should have done it. The O'Brien/Cheshire version is covered in the Guardian DataBlog and you can see from the comments that it allows people to understand in fine detail how the proposals might affect them.

Why does any of this even matter? It matters because changing parliamentary constituency boundaries is a key aspect of our democratic system and the people who represent us in parliament are tied to individual areas. Changes in these areas are not trivial and we need to be able to understand - in as much detail as possible - how proposed changes will impact upon us. It is also imperative that as many people as possible are able to see the new proposals. Publishing them in PDFs, Excel and in hard copy in a number of locations is okay but it falls well short of what we ought to expect. It shouldn't be left to mapping experts to produce interactive maps - even if it is quite enjoyable making them!

Using the current/proposed slider on the new map I can tell within 10 seconds that if the proposals go ahead I may very well have a new MP. With the spreadsheets, PDFs and hard copy map this is just not possible. Time for the Boundary Commission for England to revisit their approach in my opinion.

Saturday 20 October 2012

Data visualisation at

With so many different maps, infographics and other types of visualisations appearing on the web each day, it can be difficult for those interested in the visual display of quantitative information to keep up with the tools of the trade. Thankfully, the people at in Switzerland have put together a selection of the most powerful and useful tools currently available (see image below). 

The examples page

Click on any of the images and you'll see the flipcard format in action, whereby the image flips round and tells you where you can find out more about that particular image, tool and further technical information.

They included an example from Google Fusion Tables that I developed for a newspaper here in the UK but more interestingly they have highlighted a whole range of tools that are either quite new and unknown (e.g. CartoDB) or more for technical experts (e.g. data.js). However, there are tools here that anyone can use - even those with no technical knowledge (e.g. Google Chart Tools). Thanks to Alex Ghita for pointing this out to me.

So, definitely worth a look. I should also add that this is quite similar in nature to the examples provided in a joint CLG/OCSI project a couple of years ago which produced the DataViz pages - in relation to improving data visualisation for the public sector.

Thursday 13 September 2012

What happens when you blog?

I began this blog back in 2008 without knowing much about what I'd say or what it would become. Tech guru Alex Hardman said it would be a great idea for me to start a blog since I seemed to do a lot of visual stuff in my work. So I began blogging and decided from the start to just post what I wanted and to see where it took me. I thought I'd share a bit about my blogging experience just to demonstrate that it's not all a pointless navel-gazing exercise...

I'll start in July 2012 when I received an e-mail from legendary US-Swiss geographer Waldo Tobler saying that he noticed my map and work in the Economist (above). The June 30th edition had a special London supplement and they had asked me a little about my work on deprivation in London because they'd previously seen my work in The Guardian. I'd done a bit of work for The Guardian because Simon Rogers, the datablog editor-in-chief and all-round data journalism pioneer had seen some of my earlier work about deprivation on this blog. He'd simply contacted me to ask if I could help them get some online maps together for the release of the 2010 English Indices of Deprivation.

I started blogging about deprivation because that has been one part of my academic work for a while and there was tons of stuff (mainly maps, visuals and technical stuff) that I had produced but which would most likely never see the light of day again - and certainly not in an academic paper. Putting it on this blog means it can reach a wider audience and, more importantly, help connect me with others who have similar interests.

So, one very positive aspect of blogging (for me) has been that some of my academic-related work that would not otherwise be very widely read (i.e. it only appears in academic journals!) can reach a much wider audience. My view here is that if we're writing stuff as academics then surely we want people to read it. The blog gets somewhere in the region of 1,500-3,000 visits per month but this has taken a long time to develop. However, even when it was only getting 300 visits that meant I could still reach a much wider audience than writing academic papers alone.

The only thing that you need to do to keep the blog going is content, and that takes time so it is a bit of a commitment but I try to make my posts short and quite visual because reading from screens is just not as pleasant an experience as reading from paper. To my amazement people actually do find stuff I write and quite frequently they get in touch and we become collaborators - in a way that would not have happened had I not been blogging.

These are just a few examples of the type of things that have happened when I've been blogging. I'm sure others have had similar experiences. I'm not sure if it helps that my blog has a stupid name that I probably wish I'd never given it (!) but I try not to take myself too seriously so have not bothered changing it. At least people seem to remember it...

Monday 27 August 2012

About the Green Belt

Last week I did a short piece for the Guardian about the lack of an open map of England's green belt. This morning CPRE published a map and short briefing about the green belt being under threat (extract below). There is a national PDF map and a more detailed briefing about particular examples but there is still no way to zoom in to local areas to see it in more detail. 

The map also cites the source of the data - not the government - so that's why it can't be open and shared. I think this is a bad situation but I've said that already. I just can't believe that such an important national dataset is not in the public domain. Rant over. Nice work from CPRE though. 

Is there a link between closed green belt data and attempts to open up the green belt for development? Probably not, but there might be a relationship between the two.

Thursday 19 July 2012

The Petermann Iceberg

There's a big story in the news today about an iceberg that has broken away from the Petermann Glacier in Greenland. These things are always compared to real places in order to provide context and this case is no different. The BBC reported that the iceberg is 'twice the size of Manhattan'. This is big, but exactly how big is it? It's roughly 100 square miles. That's the same size as Edinburgh. But how big is that? To answer these questions I've just modified an earlier image based on the size of UK cities (local authority boundaries) to put it in context. Put simply, this iceberg is bigger than Bristol, it's bigger than Cardiff, it's bigger than Manchester. It's just big. Click the image below to see for yourself.

Sunday 8 July 2012

How Big is Beijing?

I was in Beijing recently for a conference and was astonished by the scale and intensity of the city. I found it a little difficult to get my head round the sheer size of Beijing so I thought I'd do a little comparison between Beijing and London. 'Beijing' here refers to the municipality of Beijing (i.e. the Province-level city) and 'London' refers to the 32 Boroughs + City of London which make up Greater London. I've illustrated this in the graphic below, which maps Beijing and Greater London at the same scale. As you can see, Beijing is much bigger - both in terms of area and population.

Click here to see the full size version.

Key facts: Beijing is, area-wise, more than ten times bigger than Greater London. It has a population of 19.6 million, compared to about 7.8 million for Greater London. This comparison is of course not really fair because Beijing includes several large rural areas. A better comparison is to look at the Inner Districts of Beijing since these cover an area quite similar to Greater London (1,377 vs 1,572 sq. km. respectively). In this comparison Beijing has 11.7 million people compared to London's 7.8 million. No matter how you look at it, though, Beijing is 'big'! If you're interested in another comparison, take a look at Oliver O'Brien's comparison of Tokyo and London...

Wednesday 13 June 2012

Why I Like 3D Maps

I do quite a bit of spatial analysis and mapping in my academic research, and some of it ends up on this blog. Over the past few years I've done quite a few 3D* maps - most recently one of population density in China. A comment by map-guru James Cheshire made me think about the 3D issue, hence this post, which attempts to say a little more about why I like using the third dimension, as it were. Also, there's not much about this stuff online at present. The two images below show population density in Europe at NUTS3 level, with a colour scale running from red (high density) to blue (low density). Click the images to enlarge.

*N.B. Data for some parts of Italy, Germany, and the UK are missing, 
but that doesn't matter for now - this is just an example.

The reason I like the addition of the third dimension with this kind of dataset is that you can tell more about the differences between areas within the same statistical category. Essentially, it adds an additional dimension of information that you can not observe from the conventional 2D map above. This is particularly true of the most high density areas in the first map. There is of course an issue here about the relative size of areas and how this might change the population density of different places but that is a different matter since I'm not in control of NUTS3 definitions! For more on this kind of thing I'd recommend looking at Stan Openshaw's work and for more on the utility (or futility?) of choropleths generally Tobler (1973) is an excellent starting point. Gale and Halperin (1982) is also worth a look.

If we assume that the main purpose of a choropleth map is to present and discover spatial patterns then there is sometimes a strong case for using the vertical dimension and extruding polygons using a z-variable (I do this in ArcScene, in case anybody is interested). However, there are some complications and I don't think it is always appropriate to go 3D. For example, depending upon the spatial structure of your data extruded polygons in one area can obscure those in another. There is also the issue of the different size of areas and the way these might have an impact upon the level of extrusion - i.e. if we used 1km cells for the European population density map it would look rather different in 3D - though this is possibly another artifact of the modifiable areal unit problem, as described by Openshaw. I've patched together three different examples from my blog in the image below, just for comparison. Another option would be to follow the example of Ben Hennig and produce population-weighted cartograms.

When I produce these 3D maps (or visualisations) I'm not trying to create a geographically precise rendering of space but rather I'm attempting to draw attention to variations in a dataset in a way which 2D maps can only do to a limited degree. They are abstractions and simplifications but in terms of understanding the world I find it can be an improvement. There is a little bit about it in this Environment and Planning B paper I wrote but I plan to write more about this in the near future (the'near future' in geological terms of course). 

*Also known as 2.5D in the GIS world, but I'll put that to one side for now...

Monday 11 June 2012

The Population of China

In a few weeks I will be travelling to China for a conference, where I'm co-presenting a paper on regional inequalities in China and the EU. This has meant I've had to get hold of lots of Chinese datasets, some of which I have produced maps from - so I thought I'd post one or two here, along with some facts and figures. I should point out that most of the data I've been using has come from the National Bureau of Statistics of China website. 

Higher resolution version

In this 3D map I've just extruded the surface using population density data at a very local level. This required a reasonable amount of computing power but the effect is simply to emphasise the significant West/East split in population within China. This geographical division has been the topic of discussion for a long time and in relation to a number of areas but is generally referred to as the Hu Huanyong line (also sometimes the Hu line, Heihe-Tengchong Line or Aihui-Tengchong Line), after the Chinese population geographer of the same name. Strangely enough, there is a Facebook page dedicated to the Hu line. The line divides China roughly into two parts. In 1935 when Hu first identified the split, the West had 57% of the area and 4% of the population. Today, the East has 94% of the population of China, but only 43% of the area. You can see how this looks in the map below... 

I'm still working on my parts of the presentation but am really fascinated by the facts and figures emerging from the 2010 Chinese census and data for different regions of China. China accounts for almost exactly 20% of the world population and both Beijing and Shanghai (i.e. the provinces) have 20 million or more people. I still need to learn a lot more about China, Chinese data and regional development there generally but my work so far suggests that patterns of regional inequality - while different in absolute terms - are often strikingly similar to patterns of regional inequality in Europe. A good example of this is in Jiangsu province, to the north of Shanghai.

Citations: Center for International Earth Science Information Network - CIESIN - Columbia University, International Food Policy Research Institute - IFPRI, The World Bank, and Centro Internacional de Agricultura Tropical - CIAT. 2011. Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Density Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). Accessed 10 June 2012.

Balk, D.L., U. Deichmann, G. Yetman, F. Pozzi, S. I. Hay, and A. Nelson. 2006. Determining Global Population Distribution: Methods, Applications and Data. Advances in Parasitology 62:119-156.

P.S. Thanks are due to Chunhua Liu for her insights here!

Sunday 3 June 2012

Red Road Demolition

A couple of years ago I blogged on the Red Road Flats in Glasgow and their imminent destruction. It wasn't quite as imminent as I thought but it now appears that next Sunday, 10th June 2012 153-213 Petershill Drive will be demolished. As someone with an interest in planning, urbanism, architecture and Glasgow this is a significant event because it represents the end of an era, and in particular the end of the great public high-rise housing experiment that Glasgow embraced. 

153-213 Petershill Drive

The delay in getting to this point is related to the process of moving the asbestos from the buildings, which you can see in this fascinating time-lapse video...

The demolition story is documented on Safedem's Red Road Demolition website and the demolition itself can be viewed live there next Sunday. I'm sure they'll also have lots of video coverage of the event soon after, as they normally do. You can find more details about the original project and architect on these pages.

For me, the most interesting and important aspect of Red Road are the stories told by people who lived there, which are captured on the Red Road Flats website.

Thursday 31 May 2012

Unemployment in Europe (via Google)

Despite recent headlines about data capture, Google remains an excellent source of (or gateway to) information on socio-demographic data. For example, if you type in 'population' followed by a country name, such as 'mexico' then this is what you'll get...

If you do this with any country you'll get the latest results plus a little graph which you can then click on and explore further. Similarly, if you type in 'eu unemployment' you will see a little chart showing EU unemployment - currently 10.2% for March 2012 - and how it has changed over time. If you click on the small chart you'll then see data for Europe and be able to add in data for other EU nations by clicking the boxes to the left. You can even embed this in a web page, as you can see below...

Apart from being convenient and accurate, this is also a very useful analytical tool when you need quick comparisons, like in the example below where I've compared Spain, the EU, Germany and Austria. As you can see the time-series data does not always extend as far back as we'd like but it is a great way to get your head round what is happening in different places without much effort at all. You'll notice in the embedded graphs that if you hover over a line it should tell you the data value for that point.

I've now changed the criteria in the chart so that it only includes unemployment for those aged less than 25 - and I've added in the UK too. This makes pretty grim reading for the EU, and Spain in particular...

This method also works for lots of other kinds of data. For example, if you type in 'us gdp' you'll see the data for the US but also have the option to add in lots of other comparators. One of the most interesting comparisons is looking at GDP over time, as you can see below.

I'm going to a conference in China at the end of June, hence my interest in national comparisons. This kind of thing has of course been covered extensively by Hans Rosling, but not many people know that it is fully integrated into Google's basic functionality.

Monday 21 May 2012

Bikes in Delft

Not a lot of data analysis or research been done on my part recently. That's because I've been on the road a bit, including attending the Regional Studies Association international conference in Delft. While I was in Delft I was not only amazed by the sheer volume of bikes but also by the way in which bikes are given their own space. I knew about this in advance but it was quite amazing to see it in action, as the short video shows. The video, by the way, was taken on my phone on a slightly windy day at the lovely TU Delft campus.

The number of bikes at Delft station was also staggering, though you can't really even begin to get the scale of it from the picture below...

While I was in Delft I gave a paper on housing market search, based on some Rightmove data. It seemed to go down quite well so hopefully during the summer I'll have time to finish the work and submit it for publication...

How did I manage to do a post without a map? I'm sure I'll rectify this next time.

Tuesday 8 May 2012

The Population of the United Kingdom

Partly inspired by a global analysis of population by latitude and longitude and partly intrigued by the latest population estimates for small areas, I've been looking at the population of the United Kingdom in more detail recently. According to the latest small area estimates (for mid-2010) the total population of the country is 62.3 million. The 2011 Census results are not out yet (release schedule here) but this figure should be pretty close to the actual number from the Census. I've been looking at where people live according to different north/south cut-offs. The series of maps below looks at (roughly) how many people live south of a) the River Thames, b) Birmingham, c) Manchester, d) Newcastle and e) Edinburgh... [click an image to see it full screen]

About a quarter live south of Thames

About half live south of Birmingham

About two thirds live south of Manchester

Nine out of ten live south of Newcastle

Over 93% live south of Edinburgh

This is not all that mind-blowing really but I was quite surprised that for the UK about half the population live south of Birmingham. The cut-off lines are slightly fuzzy because the data are based on super output areas and data zones (and local authorities for cities) but the figures are pretty accurate. 

In terms of distribution within the United Kingdom (as it still is for the time being!), 83.9% live in England, 8.4% in Scotland, 4.8% in Wales and 2.9% in Northern Ireland. There are ten times as many people in England as there are in Scotland. Or, to look at it another way, you could easily fit the population of Scotland, Wales and Northern Ireland south of London (though I'm sure they might complain).

On a more serious data-related point, I'm still baffled as to why, for example, the US and China can get some early Census results out so quickly whereas we have to wait until July 2012 for the first releases. It will be interesting to see what the final figures are.

Tuesday 24 April 2012

London's 100 Poorest Areas

The theme for this post follows on from some work I did recently which looked at the increasing level of deprivation in Outer London, as reported in the Guardian a couple of weeks ago - see also the maps on the Guardian datablog. It's true that housing market pressures (among other things) are helping to push poverty away from Inner London but the majority of London's poorest are still within the inner city, and Tower Hamlets, Hackney and Newham in particular. In order to provide a clearer picture of where exactly the very poorest (most deprived) areas are, I produced a map and animation of London's 100 most deprived areas. 

The results are not at all surprising. Of the 100 most deprived LSOAs in London, according to the 2010 Indices of Deprivation, the Borough with the most areas is Tower Hamlets (18), followed by Hackney (17), Newham (13), Haringey (12) and Brent (10). Deprivation is increasing in Outer London relative to the past and I was particularly taken today by the news that Newham Council were looking to re-house poorer residents in Stoke (more than 150 miles away). That's taking the suburbanisation of poverty a bit far! 

Some work I did with Ed Ferrari last year looked at residential mobility patterns amongst the richest and poorest sectors of the population in England. What we found surprised us at the time. Our analysis showed that it was often the poorest areas which were associated with the longest residential moves. We didn't have enough fine-grained data to make more of this but it was an interesting insight into what might be happening more broadly.

Taking the 100 most deprived locations is of course arbitrary but the point here is that despite debates about whether 'the poor' should live in 'rich areas' the fact is that many of the poorest people living in London are in areas which in recent years have changed considerably so that they are now experiencing very high demand, inflated rents and severe socio-economic inequalities. This represents an intensification of existing processes rather than something entirely new but it does mean that policy makers need to think carefully about what to do about it...