London Panopticon

Panopticon Animation

The London Panopticon utilises the traffic camera feed from the TfL API, which recently (announcement here) added ~6-second-long video clips from the traffic cameras on TfL “red route” main roads, to show the current state of traffic near you. The site loads the latest videos from the nearest camera in each compass direction to you. The images are nearly-live – generally they are up-to-date to within 10-15 minutes. If the camera is “in use” (e.g. being panned/zoomed or otherwise operated by an official to temporarily reprogramme the traffic lights, see an incident etc) then it will blank out. The site is basically just JavaScript, when you view it, your browser is loading the videos directly from TfL’s Amazon cloud-based repository.

The Panopticon continuously loops the video clips, and updates with the latest feed from the cameras every two minutes, the same frequency as the underlying source. If you are not in London or not sharing your location, it will default to Trafalgar Square. I’ve added a special “Blackfriars” one which is where the under-construction Cycle Superhighway North/South and East/West routes converge – during rush hour you can already see bursts of cyclists using the new lanes.

Try it at vis.oobrien.com/panopticon and note that it only works on desktop web browsers (I’ve tested it on Chrome, Firefox and Safari). It didn’t work on Internet Explorer “Edge” when I tested it on a PC. It also does not work on Chrome on Android and by extension probably mobile in general. It possibly uses a lot of bandwidth, so this is perhaps just as well.

I’ve named it after the Panopticon, a concept postulated by Jeremy Bentham, co-founder of University College London, where I work, in the 1800s for easy management of prisons. The Panopticon encourages good behavior, because you can’t see the watcher, so you never know if you are being watched. Kind of like the traffic cameras.

The concept evolved from a special “cameras” version (no longer working) of the London Periodic Table, which was itself a follow-on from CityDashboard, both of which I created at CASA. The source is on GitHub.

londonpanopticon

p.s. If you made it this far, you might be interested in a hidden feature, where you can specify a custom location. Just add ?lat=X&lon=Y to the URL, where the X/Y is your desired latitude/longitude respectively, in decimal coordinates. Example: http://vis.oobrien.com/panopticon/?lat=51.5&lon=0.

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High quality lithographic prints of London data, designed by Oliver O'Brien

Working Nation

leicester_industry

Top Industry maps the most popular employment for each of the ~220000 statistical small areas* within the UK. I’ve reused the “top result” (i.e. modal only) technique that has produced interesting maps for travel to work, to look at the Industry of Employment tables produced by the national statistics agencies, from the 2011 Census.

The tables I’ve used group each job into a Standard Industry Classification (SIC) category, I’ve then mapped which of these is the most popular. I’m mapping the home locations of workers, rather than where they work. I’m also only mapping where at least 20% of the working population falls into one of the categories. The “G: Wholesale retail trade, repair” category dominates through the UK – we are a nation of shopkeepers – so I’ve used a muted off-white colour to represent areas where this is the most popular. Other, rarer categories have more vivid colours.

swales_industryAll sorts of interesing patterns appear:

The map shows that the UK is far from homogenous when it comes to the industries and occupations that people work in. It reveals many areas where manufacturing remains the key employer for the local working community – typically mid-sized towns – while showing the diverse and uneven nature of the employment landscape in the larger cities. While remembering that the map is only showing the “top” (and second-top where relevant) industry category, and that other industry workers can also live in the same places, it still shows a structure and pattern consistent both with historical reasons for many of the communities’ development, but also the realities of the modern workforce, with new technology industries, and social work, becoming increasingly prevalent.

See the interactive map on CDRC Maps.
The data is available on CDRC Data.

edinburgh_industry

* Known as Output Areas in Great Britain and Small Areas in Northern Ireland.

sengland_industry

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The Battle of the Roads

ttwm_miltonkeynes

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:

ttwn_leedsbradford

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.

ttwm_stamfordhill

named

named_lennon_mccartney

named is a little website that I have recently co-written as part of an ongoing ESRC-funded project on UK surnames that we are conducting here at UCL Department of Geography. I put together the website and adapted for the UK some code on generating heatmaps showing regions of unusual popularity of a surname, that was created by researchers in the School of Computing, Informatics & Decision Systems Engineering at ASU (Arizona State University) in the USA.

The website is deliberately designed to be simple to use and “stripped down” – all you do is enter your surname and the website maps where in the UK there is an unusually high number of people with that surname living. There is also an option to enter an additional surname (for example, a maiden name for yourself or your partner, or the name of a friend) – and, by combining heatmaps of both names, we try and draw out where we think you might have met each other, or grown up together.

The Research

named_tweedy_coleOf most interest to us is the quality of the technique with pairs of surnames. It is well known already (for example, J A Cheshire, P A Longley (2012) Identifying Spatial Concentrations of Surnames, International Journal of GIS 26(2) pp309-325) that most traditional UK surname distributions remain surprisingly unchanged over many years – internal migration in the UK is a lot less than might be traditionally perceived. One of the research questions in the underlying project is to see whether this extends to marriages and other pairings too. So we encourage you to use this mode and help us understand and evaluate pairing surname distributions and patterns.

The site is also a useful information gathering tool – we are only in the early stages of evaluating the validity or accuracy of this method – we know it works well for certain regional UK names which are not too popular or too rare, at least. We ask for optional quick feedback following a search, so we can evaluate if the result feels right for you. So far, with the website been operational for around a week, nearly 10% of people are giving feedback, and around half of those suggest that it is good result for them. If it doesn’t highlight where you live now, it might be showing your ancestral home or other region that you have a historical link to. Or it may be showing complete rubbish – but let us know either way!

named_whyte_mackay

Try it out for yourself – visit here and see what it says for your surname. The site should be quite quick – it will take up to 10 seconds for names which have not already been searched, but is much faster if getting information that’s previously been searched for.

How it Works

The system is creating a probabilistic kernel density estimate (KDE), based on surname distributions (in a postcode) for an old electoral roll. It finds the relatively frequency/density of the surname compared with the general population in the area. So, in most cases, it will often highlight an area in the countryside – a sparse population, but maybe with a cluster of people with that surname. As such, it will only rarely highlight London and the other major cities of the UK, except for exceptionally urban-centric surnames, typically of foreign-origin. The method is not perfect – the “bandwidth” is fixed which means that neighbouring cities and other population fluctuations can cause false-positive results. However, we have seen enough “good” results that we think the simple has some validity, with the structure of the UK’s names.

named1

Design

On a design perspective, I wanted to build a website that looks different from the normal “full screen slippy maps” that I have designed for a lot of my research projects. Maps are normally rectangular, so I played with some CSS and a nice JQuery visual effects library, to create a circular map instead which appears to be on the back of an information disc.

Data Quality and Privacy

The map is deliberately small and low on detail because having a more detailed map would imply a higher level of precision for the underlying names data than can actually be justified. The underlying dataset has issues but is considered to be sufficient for this purpose, as long as the spatial resolution is low. Additionally, for rare names where a result may appear for only a small number of people with that name (when in rural places) we don’t want to be flagging individual villages or houses. The data’s just not good enough for that, for many names (it may well be good for some) and it may imply we are mapping exact data over someone’s house, possibly raising privacy issues – we are not, the data is not good enough for that but by coincidence it may still happen to line up with a very local feature if it was high res.

It should give an indication into the general area where your name is unusually popular relative to the local population there (N.B. not quite the same as where your name is popular in absolute terms) but I would be wary of the quality of the result if you were identifying a particular small town or exact location.

[A little update as one user worried that it was just showing a population heatmap. This would only happen for names which have a higher relative population in more dense area of the UK. Typically, older common foreign origin names will most likely show this, as foreigners traditionally migrate to cities in the UK first. The only name so far that I’ve seen it for (I haven’t tested it for many) is Zhang which is a very common surname. Compare Zhang (left) with an overall population heatmap (using the same buffer and KDE generation as the rest of the maps):

named_zhang_allpop

Some newer foreign origin names show an even more pronounced urban tendency, such as Begum and Mohammed.]

More…

Try named now, or if you are interested in surnames across the world, see the older WorldNames website, and for comparisons between 1881 and 1998 distributions in the UK, see GB Names.

If named shows “No Data” and you have entered a real surname, this may be because there are only very few of you on the UK – and in this case, I show the “No Data” graphic to protect your privacy. Otherwise I’d be mapping your house – or at least, your local neighbourhood.

ICA/Esri Cartographic Summit

cartosummit_james
I attended the Cartographic Summit “The Future of Mapping” (#cartosummit) which took place at the Esri campus in Redlands, California, earlier this month. Some notes from the week, which was co-organised by Esri and the ICA (International Cartographic Association). Here are some notes about the event, which I’ll continue to add to/tweak over the next few days.

  • The attendee list included some key names in modern cartography, including Cynthia Brewer, creator of the “ColorBrewer” set of colour ramps which I use widely in almost all my output mapping, such as in DataShine and many of the datasets on CDRC Maps
  • It was a good natured event. The only map that came in for (justified) criticism from a presenter was – unfortunately – one of my own! Former TIME graphics director Nigel Holmes (below, showing an old US election map) was perturbed to find that my Dwelling ages map seemed to be suggesting that his old house was 50 years younger than he knew it to be. The problem was compounded by some notes he referred to in this blog, which indicated a low proportion of the dwellings on the area concerned were being mapped. It is fair criticism – the detail on my map implies a level of precision that is simply not true – my counter argument being that people like to see maps of recognisable features rather than generalised blobs representing villages and towns. I think what I need to do is revisit the mapping and indicate such low proportion areas using an “uncertainty” indication such as fading out the colour…
  • James Cheshire of UCL (photo above) presented early on the conference and got straight to the point – that good maps are hard to do and, when they are done right, it’s hard to spot the effort and skill that goes into them. The proliferation of bad maps throughout the web is testament to this. He used the production process he developed for his recent book on mapping London datasets, to drive home the additional steps (shown in bold above) needed to turn a good map a great map, and reinforced the need for time – there are plenty of tools out there that allow good maps to be produced, but great maps still need care and attention.
  • Alan McConchie of Stamen talked all too briefly about the wonderful basemaps produced at the studios, including the famous “Watercolour” digital map.
  • Gary Gale of W3W looked ahead and reinforced the point that far from being an old-style industry, cartography has never been more current or key.
  • Ken Fields of Esri gave us a dizzying tour of new cartography that he has been experimenting with over the last couple of years. He also gave a sneak peek of a very interesting looking book that he is currently working on…
  • There was good academic representation in the audience, however there were some notable gaps. Commercial considerations are understandable but it was a pity there were no representation from Google, HERE, CartoDB or – especially – MapBox. The digital cartography groups within these organisations are producing great things. MapBox, in particular with its huge number of GitHub open source projects such as CartoCSS. MapBox did get a mention in one of the later talks, relating to Esri’s ongoing work to implement the MapBox Vector Tiles (MVT) format. The absence is perhaps reflective of Esri being the co-sponsor and host, who may therefore be reluctant to provide the other organisations with a high-profile platform but it still remains the fact that no discussion of modern digital cartography can be complete and representative without including the excellent work by these groups. Having said that, the small guestlist and excellent facilities provided for breakouts and discussion, allowed for good networking opportunities and gave everyone time to discuss cartographical insights with key professionals, an opportunity likely not afforded at a larger, less focused event.

cartosummit_nigel

My key take-away from the event is that digital cartography is now more important than ever. The plethora of tools available in the “market” now for creating maps has never been larger, but the need to create maps, which present the data fairly and impartially while engaging the viewer and encouraging them to explore, is just as critical as it has ever been. Anyone can make a map now, but creating a great map is very much a skill.

A very timely, useful conference and very much shows the need for a dedicated cartography track at the major industry and academic conferences in the GIS/geovis/datavis fields.

cartosummit_attendees

What if There Were No Cars?

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):

traveltowork_car

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:

traveltowork_nocar

(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:

traveltowork_nocar_fish

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.

traveltowork_nocar_cambridge

The Age of Buildings

liverpool_houseages

We don’t have individual building age open data in the UK, unlike in some other countries (the data has been used to great effect in New York City and Amsterdam) but the Valuation Office Agency, which amongst other things decides council tax bandings for residential properties, has published some interesting data on how old houses are in England and Wales – it’s their “dwelling ages” dataset. A separate governmental organisation, the ONS, publishes house prices summaries, at a relatively small-area* scale, on a quarterly basis for the previous year. I have combined both these datasets into a record on CDRC Data. and have mapped them both on CDRC Maps.

bristol_houseagesThe dwelling age data is supplied grouped in approximately ten-year age bands (+ a Pre-1900 catch-all) with a count of the number of houses in each band, for each small area (LSOA) in England/Wales. I’ve mapped just the modal band, that is, the band with the most number of houses in it**. In some cases, houses were steadily built in an area throughout the 20th century, so that the band assigned to that area is not actually very representative of the houses there – this can be spotted by looking at the “Classif. %” number which appears on the right.

Many UK cities show a pattern of Pre-1900 inner-city (dark grey on the map), with early 20th century houses out towards the edge (lightening blues). The “Green Belts” of the 1940s stopped this radial outward development, so, some old housing was instead overhauled to build 1960s-70s housing estates (shown in yellow) and more recently, the urban core has seen much of the recent housebuilding activity. This shows up on the map as an area of red in the centre of many cities. There are some exceptions – Milton Keynes is a large, and new, town, its map showing mainly yellows and reds.

Not all areas are constrained by Green Belts but some have other, physical constraints, such as the sea. Weston-super-Mare, for example, has steadily expanded westwards over the last 150 years:

westonsupermare_ages

A second map concentrates just on post-WW2 (1945+) building, showing the proportion of such houses in each area. Hello, riverside east London:

london_riverside

The house price pattern in England/Wales is quite familiar to many people – basically London is eye-wateringly expensive, particularly in the central and west, along with some satellite towns and cities (e.g. Oxford and Cambridge) but not others (e.g. Luton and Harlow). I’ve mapped the median house prices for each small-area as I think this better provides an indicator of a typical price paid. 50% of properties sold in the previous 12 months, in each area, sold for less than this amount, and 50% for more. As only a few houses in an area typically get sold in a year (I have included this number in the metric data) it is worth noting that the values can jump around a lot.

Explore the interactive maps:

houseprices

* There is separately individual house transactions (with prices) released regularly by a third organisation, the Land Registry, however I have not mapped this at this time.

** Where an area is fairly equally split between two bands, I’ve included the “runner up” band as well, shown thinner vertical stripes. This only appears where the runner up housing count is 90% of the modal band, and the two bands account for more than half of the total housing. I’m using Mapnik compositing operations to get the vertical stripes, rather than a very long and repetitive stylesheet. I calculated the modal band in Excel from the original VOA dataset by using MAX (to find the value) and nested IFs (to display the category). Calculating runner up (i.e. second from mode) was a little more tricky, but I was able to do this but using COUNTIF and LARGE (to find the value – which could the same as the mode, ie. multimodal) and then nested IFs/ANDs to display the category.

London’s Bikeshare Needs A Redistribution of Stations

bikes_journey_day

Here’s an interesting graph, which combines data on total journeys per day on London’s bicycle sharing system (currently called “Santander Cycles”) from the London Data Store, with counts of available bicycles per day to hire, from my own research database. The system launched in summer 2010 and I started tracking the numbers almost from the start.

You can see the two big expansions of the system as jumps in the numbers of available bikes – to all of Tower Hamlets in early 2012, and to Putney and Fulham in late 2013. Since then, the system has somewhat stagnated in terms of its area of availability, although encouragingly at least the numbers of available bikes has remained constant at around 9500, suggesting that at least the operator is on top of being able to maintain and repair the bikes (or regularly source new ones). Some of the individual bikes have had 4000 trips on them. There is a small expansion due in the Olympic Park in spring 2016, but the 8 new docking stations represents only a 1% increase in the number of docking stations across the system, so I doubt it will have a significant impact on the numbers of available bikes for use.

There is a general downward trend in the numbers of uses of each bike per day, since the halycon Olympic days of Summer 2012, over and above the normal seasonal variation, which concerns me. The one-year moving average recently dipped below 3 uses of each bike per day, this summer, and I am not confident it will pick up any time soon. (The occasional spikes in uses/bike, by the way, generally correspond to sunny summer bank holidays, tube strikes and Christmas Day).

To rejuvenate the system and draw in more users, rather than relying on the established commuter and tourist flows which likely dominate the current usage, I am convinced that the system needs to expand – not necessarily in terms of the number of bikes or docking stations, but in its footprint. I think the system would be much improved by dropping the constraining rule on density (which approximates to always having one docking station every 300m) and instead redistributing some of the poorly performing docking stations themselves further out. It’s crazy that, five years on, there are no docking stations in central Hackney, Highbury, or Brixton, three areas with an established cycling culture and easily cycle-able into the centre of London. Conversely, Putney and Tower Hamlets simply don’t need the high density of docking stations that they currently have, except in specific areas (such as around the train/tube stations in Putney, and Canary Wharf).

Ideally we would have a good density of docking stations throughout cycleable London but, as docking stations (and bikes) are very expensive, I would suggest that TfL instead adopts the model used in Bordeaux (below). Here, the city retains a high-dense core serving tourists, commuters and other centrally-based workers, but adopts a much lower density in the suburbs, so that, while tourists can still “run into” docking stations they don’t know about in the centre thanks to the high density, local users can benefit from the facility in their neighbourhood too, even if it requires a little longer walk to get to it.

bikes_bordeaux

Technical note: Before November 2011, the London numbers included bicycles that were in a docking station but not available to hire (i.e. marked as broken). This exaggerates the number of available bikes (and correspondingly reduces the number of hires/bike/day from the true value) in this period by a small amount – typically around 3-5%, an effect I am not considering significant for this analysis.

In/Visibility and Difference – Visual Methods Workshop in Berlin

vmw1

I presented a talk on geodemographic mapping, at a visual methods workshop “In/Visibility and Difference” which took place in Berlin at Bard College (formerly the European College of Liberal Arts). The workshop was organised by the TransformIG project at Humboldt University in Berlin, which was also the venue for the keynote part of the meeting. Thank you to the organisers for organising an interesting and intensive workshop which presented a wide variety of visual and geographical techniques which are becoming key ways to structure and analyse sociological studies.

I structured my talk into four sections:

  1. An primer on improving choropleth mapping of socioeconomic data, moving beyond the basic “heat map” by adding regular geographical feaures (see photo below), labels and clipping coverage to populated areas, to explain the demographic patterns and highlight external influences. This is the technique used by DataShine to display Census 2011 aggregate statistics, and CDRC Maps to show geodemographics. I also outlined alternative approaches used by other research groups, such as cartograms and dot density maps.
  2. A tour of the geodemographic maps in CDRC Maps, including the Output Area Classification and a map of the latest Index of Multiple Deprivation. I also touched briefly on the problems of geodemodemographic classifications, where good/poor fits to the classification are typically mapped identically, and the “second choice” classification doesn’t get shown, showing some techniques to try and map these subtleties.
  3. An introduction to more novel methods of mapping demographic data, such as Lives on the Line and Tube Tongues, but highlighting the shortcomings of such maps too.
  4. Finally, a brief mention of mapping more novel datasets, showcasing the Twitter language maps for London and New York – again discussing the flaws as well as strengths of such maps.

vmw_me

I found many of the other talks very interesting – particularly the work by plan b – two performance artists who have essentially tracked their entire outdoor life over the last 8+ years, both creating GPS traces which they have turned into artworks at various scales and on mediums (including a 3D mould), but also temporal activity indicators which they have grouped together into small multiples. They term these the “birch trees” due to their characteristic stripy white/black columns (see top photo). I also liked the striking pictograms created by Migrantas who have created simple and powerful graphics, from stories from the migrant community in Berlin and elsewhere. Their work can be seen on billboards and walls in various places across the city. There was a good talk by Stefan Lindemann on “SuperLUX”, focusing on linear development along commuter lines to Luxembourg City and corresponding population changes – essentially an international take (due to the country’s size) of the more recent “Northern Powerhouse” project to connect the cities of the north of England.

There was one more map “treat” for me at the workshop – the closing keynote given by Caroline Knowles included her investigative journalistic project where she tracked the complete journey of a pair of flip flops – from oil in Kuwait, to factories in South Korea, then to and through the markets in east Africa, to the consumer, and then finally to the rubbish dumps of the region. A map illustrating the life cycle of the flip flops is below.

Thanks to Agata Lisiak and the TransformIG team for inviting me to speak at the workshop and the opportunity to learn as well as disseminate. (Photo credit for the top photo: Agata).

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Changes in Deprivation in England, 2010-15

Click any of the images in this article to go to the interactive map.

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Above: A significant reduction in relative deprivation in Blackheath and Maze Hill since 2010.

I’ve just now published a number of maps on the CDRC Maps platform which uses the DataShine mapping style (more about DataShine) to show demographic data relating to consumer and other datasets.

The maps relate to the Indices of Deprivation 2015, small-area measures of deprivation in England, which were compiled and published at the end of September by OCSI on behalf of the UK Government.

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Above: Deprivation varies between Tottenham, Walthamstow and Woodford Green, in 2015.

The Indices of Deprivation (of which the Index of Multiple Deprivation, or IMD is the overall index) split England into around 32000 areas (“LSOAs”), each containing a typical population of 1500. Each area is scored for several components, which are then combined (with different weights) to produce an overall score of deprivation for the area. Note that areas with little deprivation may be mainly compared of people who are not “wealthy” but just not deprived, and therefore rank the same as areas mainly populated by extremely affluent people. IMD is a measure of deprivation, not affluence.

The look of these maps, with their Red-Yellow-Green colour ramp, is intentionally similar to my New Booth map of the 2010 IMD deciles which was my first “colour the houses” map and the precursor to DataShine and therefore CDRC Maps.

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Above: Milton Keynes has a characteristic strip of high deprivation, running north/south.

These scores cannot be directly compared with those from previous exercises (2010, 2007 and 2004 are the recent ones) due to slight methodological alterations, however we can rank each area based on the overall score – this is the Index of Multiple Deprivation – and then compare ranking changes between the years. It should be noted that a decrease in rank (i.e. an increase in deprivation measure compared with other areas) does not mean that an area has become more deprived in absolute terms – it may be just becoming less deprived at a slower rate. I have mapped the overall rank change from 2010 to 2015, and also the rank change of the component which measures the effects of crime on deprivation, as this shows some particularly interesting spatial characteristics.

Looking at the overall changes, London’s pattern is striking:
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Above: London has an inner-city “ring” of blue showing a large reduction in relative deprivation since 2010.

London’s inner city areas – Zones 2-4 – have becoming significantly less deprived in the last year. Indeed London, in general, has done very well recently relative to the rest of England, with only a few areas (St John’s Wood, Thornton Heath, Mill Hill, East Barnet and Hounslow) showing a significant increase in relative deprivation levels. Again, this may mean that they are still becoming less deprived, just at a slower rate. By comparison, Blackheath, Ealing, Upton, North Wembley and Crouch End have become dramatically less deprived since 2010. There are smaller pockets throughout the city who are are also showing marked moves in both directions – see the interactive map. I use a different (Red-White-Blue) colour ramp for these maps, to emphasise that they are showing changes.

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Above: The contribution of crime to deprivation has significantly dropped in Reading and increased in Bury.

Some of the more notable results for changes in the crime component ranking of the IMD are in Reading (where the impact of crime on deprivation has significantly reduced) and Bury (where it has had a significantly greater impact). In both towns (see above, presented at different scales) however, other components have acted in the opposite direction, such as the deprivation ranking of these two places, with respect to the rest of England, has not significantly changed in five years. Bury, was, and still is, already significantly more deprived than Reading, the difference between the two has increased.

Another example: comparing Gateshead with nearby South Shields. The former coming up, the latter going down:
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Gateshead is almost universally moving out of deprivation at a faster rate than the rest of England, while South Shields is change much more slowly.

The components are income, employment, education, health, crime, barriers to housing and services, and living environment. Their weights are summarised in this nice infographic from gov.uk.

There is also an official summary which maps the data slightly differently. One of its analyses – Chart 6 – shows the local authorities (LA) where relative deprivation has significantly fallen, by measuring the proportion of areas within the LA that have moved out of the bottom 10% in the IMD, between 2010 and 2015. The top four are: Hackney, Tower Hamlets, Greenwich and Newham. These are four of the five Olympic Boroughs. The fifth, Waltham Forest, is also in the top 10. East London is changing.

See these maps and various geodemographic classifications at CDRC Maps.

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Across middle England, cities are more deprived than the countryside, with notable exceptions (such as Shrewsbury, Cambridge, northern Leeds and western Sheffield).