Category: Geodemographics

Following on from my two maps of the small-area modal method of travel to work – one map includes cars and so is most interesting for London, and one map excludes cars and so is most interesting for the rest of the country, where cars otherwise dominate – I’ve refined the car-excluding map and introduced a third one.

The Refinement: Map Meaningful Results Only (>10%)

I was mapping the second most popular method of travel to work, after cars. But for many areas, there is no sensible second method. Therefore, I was often mapping results for extremely small numbers of people. One person cycling to work while the other 199 drive, in a small area, does not tell us much. The more interesting result is the lack of a strong non-car travel mode. So, I’ve refined the map to remove the colour where it is representing less than 10% of workers. For areas with good public transport options, or a strong tradition of working from home or walking to work, the map is largely unchanged, but for other areas which are sadly dominated by the motor car, the map now shows large areas of grey.

Four notable areas of greyness are Milton Keynes, the Welsh Valleys, Telford and Middlesbrough. In large conurbations, distinct areas appear, such as Walderslade near Chatham, the outer parts of Swansea or the eastern half of Cannock. London changes little, the nearest car-only area being Sunbury on Thames, which is technically just outside London but within its sphere of influence, if not its public transport options.

Additionally, I previously was also showing a “runner up” non-car mode of travel, where this fell only slightly behind the main mapped mode. This additional mode was shown using vertical stripes. However, I’ve tweaked this so that it is instead always included if, like the primary non-car mode, it also represents at least 10% of workers there. This has most effect in London, where buses are in fact widely used right across London, even if the tube/train mode is also heavily used. So London now is mainly composed purple stripes (bus) on top of orange (tube/train). This change also brings out the cycling mode in a number of other cities, notably Hull and Bristol, cities where public transport is well used – and so was masking the still-popular cycling mode.

The New Map: Road Users Only

I’ve introduced a third map, focusing on road travel only – so I’ve eliminated tube/metro use, as well as working from home.

Do the buses beat the cars (remember though, there are more people on a typical bus than in cars), or do the cyclists beat both? Well – mainly it’s the cars, with most cities and other urban areas showing a walking core, surrounded by cars. Inner city areas often have bus use appearing, typically as a second-place usage (I only show here, unlike above, it where it nearly is as widely used as the main mode) in some sectors, but not others. Leeds and Bradford both show this pattern:

London is the stand-out exception with heavy bus and bike usage, across wide areas well away from the centre. Taxis are also popular with the rich of Kensington.

But in some towns and cities, even the central walkers lose out. Telford is one place with no walking core. Wellingborough/Rushden is another, and Boston. Margate/Ramsgate’s is unusually small. In these places, at least, the car is king of the roads, throughout the urban realm.

Note: Like all the travel to work maps, I’m mapping small-area statistics for the residential location (i.e. home) of people that are of working age. Each small area has a typical working population of around 200 and typically represents two or three average-length residential streets. The maps include non-residential buildings by necessity, as I do not have data to eliminate these, but the colours/stats only represent the nearby residential population, not people who work in these buildings.

Here’s a map of the top method of travel to work, for each “small area” (~250 people) in the UK, for people aged 16-74 and in employment, at the time of the 2011 Census (or try the interactive, zoomable version):

The pattern is, fairly evenly, that car use (light blue) dominates except for people living in the very centre of cities, where walking to work (green) is the most popular method. The two big exceptions are London, where rail/metro travel (orange) dominates for the inner city zone, separating the walking core and car-driving outer London ring; and Cambridge, where the cyclists (red) really are king. There are some other interesting results in small areas (e.g. walking is popular in central Leicester but not in the centre of Peterborough), but overall, the map doesn’t tell you much more.

So, I’ve considered what the map would look like if we removed cars from the calculations – what form of transport is used by the people that need to work but don’t own or otherwise have access to cars, either as a driver or passenger? How does the UK commute, without cars, right now – and what might a UK landscape look like without the great rush-hour traffic jam, if the alternatives, pro-rata, were adopted? A whimsical hypothesis – cars are always going to be essential for certain kinds of commutes in certain parts of the UK – but let’s see what happens anyway, as it will still tell us something about public transport provision, city walkability and maybe attitudes to working life in general.

Here is a map of the top carless commute method for small areas, right across Britain:

(Here is the interactive, zoomable version).

Suddenly, all sorts of interesting trends emerge. In rural areas, working from home dominates – with no public transport, and motorbikes being an uncommon form of transport in the UK, this is the only option. In towns and villages, and in city centres, walking to work dominations. Both are obvious – the interesting results appear if you zoom in:

• In London, the central walking-to-work area (green) coincides almost perfectly with the congestion charge zone. Other walking areas include the large outer London town centres of Hillingdon, Croydon and Kingston that have been absorbed into the metropolis, and the traditional community of Stamford Hill.
• Rail/metro (orange) dominates throughout Zones 2-6 London and beyond.
• London has four major areas of bus dominance (purple) – Burgess Park in the south, Hackney in the north-east, the western Lea Valley in the north and a huge zone surrounding Heathrow Airport in the west. Three of these not surprisingly coincide with areas of poor rail/metro provision, but the western Lea Valley result is interesting – there are two rail lines down through this area with stopping services. However, notably, this area’s most popular employment type is cleaning – cleaners typically have to work nights, where the bus is the only public transport option.
• York versus Leeds – both have a similarly sized walking core, but then the rest of Leeds has bus users, while York’s outskirts are dominated by cyclists (red). The flatter nature of York is likely the major reason.
• Buses are pretty crucial in the Birmingham conurbation.
• Cycling dominates in almost every part of Cambridge but less so in the other famous cycling city, Oxford. In London, Hackney’s famed cycle community actually has roughly equal prominence with both bus and train/metro use.
• Stoke-on-Trent has a very large walking core, larger than for the larger cities, covering the whole area almost, rather than being surrounded by bus/cycling/train commuters as normally happens. Stoke-on-Trent is actually a conurbation of six towns, with employment scattered throughout rather than concentrated in the normal core. Alternatively this could be due to poor bus provision or a dominance of driving.
• Ilkley and Bingley like their trains – nearby Keighley and Skipton, nearby and on the same network, don’t. The former two towns perhaps act more as commuter towns for Leeds while the latter two have a tradition of more local employment.
• The very richest areas have a high proportion of people working at home (brown) – live in help, aka domestic servants? See Knightsbridge and Hampstead Garden Suburb in London, or Sutton Park near Birmingham, are two examples.
• The new towns in central Scotland seems to have a greater proportion of working-at-home than equivalent new-town areas in England.
• Fishing communities (yellow – other) are obvious in north-east Scotland:

These are just a few of the spatial patterns I’ve spotted – there are I’m sure many more interesting ones. Sometimes, removing the dominant factor reveals the interesting map.

The technique of mapping only the most dominant mode of transport has a serious flaw, in that, depending on how you merge or split other transport modes, you can significantly influence which appears “top”. I have merged some modes together (driver+car passenger, train+metro+tram, and taxi+motorbike+other, e.g. boat), hopefully in a meaningful way that shows interesting results without hiding the bigger picture. Another mitigating factor is that, where a second mode of transport has nearly as much use as the first, I include its colour too, in narrow vertical banding, and highlight this in the interactive “area information” panel.

All the maps in this article use the CDRC Maps platform, created by the Consumer Data Research Centre, to map small-area consumer and other demographic data for the UK. Because I am using Census data, I am able to map for the whole of the UK (including Scotland and Northern Ireland), as, for the Census at least, the activity is coordinated across the nations, and while the outputs are arranged differently, they are sufficiently similar to combine and use together with care. The data comes from the National Statistics agencies – the ONS, NRS and NISRA, and is Crown Copyright, licensed under the Open Government Licence.

Have a look at some other CDRC datasets mapped, download the data yourself or find out more about the CDRC.

The 2011 Area Classification for Output Areas, or 2011 OAC, is a geodemographic classification that was developed by Dr Chris Gale during his Ph.D at UCL Geography over the last few years, in close conjunction with the Office for National Statistics, who have endorsed it and adopted it as their official classification and who collected and provided the data behind the classification – namely the 2011 Census.

A geodemographic classification such as this takes the datasets and looks for clusters, where particular places have similar characteristics across many of the variables. It does this on a non-geographic basis, but spatial autocorrelation means that geographic groupings do typically appear – e.g. a particular part of an inner city will typically have more in common with another part of the inner city, than of the suburbs. However, these areas will often also share much in common with other “inner city” parts of cities elsewhere. Names are then assigned, to attempt to succinctly describe the clusters.

As part of the DataShine project, we have taken the classifications, and mapped them, using the DataShine style of restricting the classification colouring to built up areas and (when zoomed in) individual rows of houses. The map is the third DataShine output, following maps of individual census tables and also the new Travel to Work Flows table.

We’re just mapping the eight “Supergroups”, the top-level clusters. A pop-up shows the more detailed groups and subgroups, and you can find pen-portraits for all these classifications on the ONS website.

Click on the box for an individual supergroup, in the key at the top, to see a map showing just that supergroup on its own. For example, here are the “Cosmopolitan” dwellers of London:

Like 2011 OAC itself, the map covers all of the UK, including Scotland and Northern Ireland. For the latter, there is no Ordnance Survey Open Data which is how we created the building/urban outlines, so we have improvised with data from OpenStreetMap and NISRA (Northern Ireland Statistics).

The map is part of DataShine, an output of the BODMAS project, but also is in conjunction with the the new Consumer Research Data Centre, an ESRC Data Investment which is being set up here at UCL and other institutions. As such, there is a CDRC version of the map.

As part of the BODMAS project we have also been studying the quality of fit of 2011 OAC for different parts of the UK, and techniques to visualise the uncertainty and quality of the classifications. We will be presenting these findings at the Uncertainty workshop at the GIScience conference in Vienna, later this month.

Direct link to the map.
See also the DataShine blog.

This week, James and I launch DataShine: Census. This is part of the ESRC BODMAS project, here at UCL’s Centre for Advanced Spatial Analysis, that is led by James, and which started at the beginning of this year.

DataShine: Census shows web maps of the Quick Statistics aggregate tables of Census data for England/Wales for 2011, that were published last year by the Office of National Statistics.

DataShine: Census is the successor to CensusProfiler which I put together when I was at UCL’s Department of Geography in 2009. The main difference, apart from being a more modern website with updating URLs, geolocation etc, is that the data maps presented are “shone” through buildings, rather than covering all the land area. This has two advantages, and two disadvantages. The two advantages are that it means the countryside doesn’t dominate, and that the urban form (building blocks, parks, road structures) is more recognisable – so it looks more like a map of real places rather than a complicated patchwork of bright colours with abstract boundaries. The two disadvantages are that buildings can be individually represented, implying a greater level of spatial precision than is the case.

For the Census data, I wanted to come up with a good way of showing an interesting map, for all ~900 census aggregate variables, without having to make 900 decisions manually. To do this, I calculated the average percentage population, based on the populations across the output areas (~150 houses each), and the standard deviation of the percentage population. When you do this, and then plot the two statistics for each variable against each other, you get a graph like this:

Most variables have very small averages and so cluster at the bottom left hand side. The distinctive line of variables with small averages and high standard deviations are where the overall population is care homes and other institutions, rather than people or standard households.

I have split the variables into four sections, each of which is grouped differently for the key. The ones under the main triangle are mapped using a divergent colour scheme (red/green by default) from the average, which always appears in the middle of the key:

The ones above it (high standard deviations) are mapped as simple equal intervals of eighths, between 0 and 100%:

Finally, variables with very small/large averages, and large standard deviations, are mapped as multiples of the average (or 1-average) – here the average will always appear one from the beginning or the end of the key:

(The other three are using sequential colour ramps.)

DataShine is a platform for creating these kinds of web maps. As well as the initial census example, we are hoping to use it create other sorts of web maps, I hope to release and blog about those soon! I am also running a dedicated DataShine blog, which currently features some examples of particularly interesting maps coming from DataShine: Census, as well as some technical detail of the “geostack” behind the platform.

James has also written about the project.