All posts by Oliver O'Brien

Evolution of London’s Rush Hour Traffic Mix

My latest London data visualisation crunches an interesting dataset from the Department of Transport. The data is available across England, although I’ve chosen London in particular because of its more interesting (i.e. not just car dominated) traffic mix. I’ve also focused on just the data for 8am to 9am, to examine the height of the morning rush hour, when the roads are most heavily used. 15 years worth of data is included – although many recording stations don’t have data for each of those years. You can choose up to three modes of transport at once, with the three showing as three circles of different colours (red, yellow and blue) superimposed on each other. The size of each circle is proportional to the flow.

It’s not strictly a new visualisation, rather, it’s an updated version of an older one which had data from just one year, using “smoothed” counts. But it turns out that the raw counts, while by their nature more “noisy”, cover a great many more years and are split by hours of the day. I’ve also filtered out counting stations which haven’t had measurements made in the last few years.

Note also the graph colours and map colours don’t line up – unfortunately the Google Material API, that I am using for the charting, does not yet allow changing of colours.

An alternate mode for the map, using the second line of options, allows you to quantify the change between two years, for a single selected type of transport. Green circles show an increase between the first and second year, with purple indicating decreases.

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

How Mexico City Does Bikeshare

The above map shows the estimated routes and flows of over 16 million users of the bikeshare in Mexico City, “ECOBICI“, across the 22 months between February 2015 and November 2016, using data from their open data portal. The system has been around since 2011 but its most recent major expansion, to the south, was in early February 2015, hence why I have show the flows from this date. The wider the lines, the more bikeshare bikes have been cycled along that street. The bikes themselves don’t have GPS, so the routes are estimated on an “adjusted shortest route” basis using OpenStreetMap data on street types and cycleways, where any nearby cycleway acts as a significant “pull” from the shortest A-to-B route. Having cycled myself on one of the bikes in November (and hence my journey is one of the 16.6 million here) I fully appreciate the benefits of the segregated cycle lanes along some of the major streets. As my routes are estimates, they don’t account for poor routes taken by people, or “tours” which end up at the same places as they started. So, the graphic is just a theoretical illustration, based on the known start/end data.

The bikeshare journeys are in a dark green shade, ECOBICI’s brand colour, with docking stations shown as magenta dots. Magenta is very much the colour of CDMX, the city government, and it consequently is everywhere on street signs and government employee uniforms. Mexico City doesn’t have rivers, which are the “natural” geographical landmark for cities like London and New York where I’ve created similar maps, so I’ve used the motorways (shaded grey) and parks (light green), to provide some context. Mexico City extends well beyond the ECOBICI area.

The maps shows huge flows down the “Paseo de la Reforma”. This route is always popular with cyclists, thanks to large, segregated cycle lanes in both directions, on the parallel side roads. On Sunday mornings, the main road itself is closed to motor traffic, along with some other link routes. This is not reflected in my routing algorithm but also acts to increase the popularity of the flow in this general area. To the north, a cluster of docking stations and a large flow indicates the location of Buena Vista station, the only remaining commuter rail terminal in Mexico City. Further south, the curved roads around Parque México and Parque España are also popular with bikeshare users, in this leafy area that very much feels like the “Islington” of Mexico City:

Mexico City’s ECOBICI is one of the 150+ systems I’m tracking live on Bike Share Map. You can see the live situation, or an animation for the last 48 hours.

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

Lives on the Line v2: Estimated Life Expectancy by Small Areas

livesontheline_district

I’ve produced an updated version of a graphic that my colleague Dr James Cheshire created a few years ago, showing how the estimated life expectancy at birth varies throughout the capital, using a geographical tube map to illustrate sometimes dramatic change in a short distance.

You can see an interactive version on my tube data visualisation platform. Click a line colour in the key on the bottom right, to show just that line. For example, here’s the Central line in west London.

The data source is this ONS report from 2015 which reports averages by MSOA (typical population 8000) for 2009-2013. I’ve averaged the male and female estimates, and included all MSOAs which touch or are within a 200m radius buffer surrounding the centroid of each tube, DLR and London Overground station and London Tram stops. I’ve also included Crossrail which opens fully in 2019. The technique is similar to James’s, he wrote up how he did it in this blogpost. I used QGIS to perform the spatial analysis. The file with my calculated numbers by station is here and I’m planning on placing the updated code on GitHub soon.

livesontheline_alllondon

My version uses different aggregation units (MSOAs) to James’s original (which used wards). As such, due to differing wards and MSOAs being included within each station’s buffer area, you cannot directly compare the numbers between the two graphics. An addition is that I can include stations beyond the London boundary, as James’s original dataset was a special dataset covering the GLA area only, while my dataset covers the whole of England. The advantage of utilising my data-driven platform means that I can easily update the numbers, as and when new estimates are published by the ONS.

Estimating life expectancies at birth for small areas, such as MSOAs, is a tricky business and highly susceptible to change, particularly due London’s high rates of internal migration and environmental change. Nevertheless it provides a good snapshot of a divided city.

View the interactive version.

livesontheline_dlr

Data: ONS. Code: Oliver O’Brien. Background mapping: HERE Maps.

Smart Mobility Meeting in Mexico City

Below is a presentation that combined my talks last Thursday and Friday at the Smart Mobility forums in central Mexico City, organised by ITDP Mexico and funded by the Foreign and Commonwealth Office’s Prosperity Fund (respresented by the British Embassy in Mexico). The Thursday presentation focused on the third-party app ecosystem that exists around bikesharing in London and elsewhere, while the Friday presentation included more examples of private sector innovation using open data:

My week in Mexico City also included a visit to CIC at IPN (the computational research centre city’s main polytechnic) where I was introduced to a product building visualisations of ECO-BICI data to help create more effective strategies for redistribution. I also visited LabCDMX, a research group and ideas hub to study Mexico City that has been created by the city government, to give a couple of talks in their rooftop on visualising London transit and a summary of web mapping technologies. The organisers also squeezed in a couple of short TV interviews, including Milenio Noticias (23 minutes in). The week ended with a tour of the ECO-BICI operations, repair, management and redistribution warehouse, located centrally and a hive of activity. This included a look at their big-screen redistribution map and vehicle routing system.

Some of the companies and products I cited included CityBikes, Cycle Hire Widget, TransitScreen, ITO World, Shoothill, Waze, Strava Metro and CityMapper. I also showed some academic work from myself, James Cheshire and Steve James Gray in UCL GSAC and UCL CASA respectively, an article in The Guardian by Charles Arthur, an artwork by Keiichi Matsudaa and a book by James Cheshire and Oliver Uberti. I also mentioned WhatDoTheyKnow and heavily featured the open data from Transport for London.

I also featured some work of my own, including CDRC Maps, TubeHeartbeat, London Panopticon, Tube Stats Map, CityDashboard, Bike Share Map and London Cycling Census map.

ecobici

Twelve Talks

November is shaping up to be a very busy month for me, in terms of giving talks – I will have presented 13 times by the end of the month. I appreciate that lecturers might not agree that this is a particular busy month! Anyway, here’s a list of them:

  1. 1 November – CDRC Maps: Introduction and Impact (10m)
    Audience: ESRC/Moore-Sloan Meeting
  2. 3 November – Guest Lecture & Practical: Web Mapping (60m + 2h)
    Audience: Second Year Geography Undergraduates at UCL
  3. 9 November – Research Lab Update: Worldnames & CDRC Maps (3m)
    Audience: Jack Dangermond Keynote Lecture at UCL
  4. 11 November – London: Visualising the Moving City (30m)
    Audience: EU COST Action London meeting
  5. 15 November – CDRC Maps: Introduction (5m)
    Audience: Academic visitors from South Korea
  6. 17 November – London: Visualising the Moving City (60m)
    Audience: Geospatial Seminar Series (UCL CEGE)
  7. 22 November – Data visualisation for Bikeshare Systems (60m)
    Audience: CIC-IPN staff and students (Mexico City)
  8. 22 November – Web Mapping (60m)
    Audience: CIC-IPN students (Mexico City)
  9. 23 November – London: Visualising the Moving City (60m)
    Audience: Public officials and students (Mexico City)
  10. 23 November – Data visualisation design workshop (60m)
    Audience: ITDP staff (Mexico City)
  11. 24 November – Third-party App Ecosystems using Open Data (45m)
    Audience: Public officials (Mexico City)
  12. 25 November – Open Data and Innovation for the Private Sector (60m)
    Audience: Small businesses (Mexico City)
  13. 28 November – CDRC Maps: Introduction (5m)
    Audience: Academic visitors from Japan

I have also contributed material for a further talk given by a colleague – an introduction to geodemographics in the UK, for the Brazil governmental statistical service.

Taxonomy of Web Mapping Frameworks and Formats

Here’s an attempt to create a simple taxonomy of the currently active and popular web mapping frameworks available. This covers web mapping that delivers a consumer-navigable geographic “slippy” map of raster and/or vector tiles containing bespoke geographic data.

FRAMEWORKS
< < < EASY, costs, limited, quick
Flexible, Needs resources, time, HARD > > >
Ecosystems Hosted Wrappers Managed Wrappers Managed APIs Open Frameworks Spatial Servers Server Programming
Mapbox Studio


CARTO Builder


ESRI ArcGIS Online


Tableau

Google Fusion Tables


Google MyMaps
Google Maps Embed API


Google Static Maps API


OSM StaticMapLite
HERE Maps API for JavaScript


Google Maps JavaScript API


Microsoft Bing Maps V8 SDK
OpenLayers


Leaflet


D3 DataMaps


Leaflet for R/RStudio


RMaps
MapServer


GeoServer
R (ggplot)


Unfolding (Processing/Java)


Mapnik (C++/Python)
Capabilities/Requirements of the above Frameworks
Data analysis Data analysis
Remote server dependency Server with shell access required
Web space required
Scripting knowledge required Programming required

I will aim to update based on feedback and new discovery. This initial version is based on my own usages/experiences in the field, so it is quite possible there are some very obvious candidates I have missed.

Additionally (and with the some proviso as above) here’s a 2×2 table of file formats used in slippy and static web mapping, for vectors and rasters – the latter including attribute fields like UTF Grids. I am only including formats widely used in web mapping, rather than GIS in general.

DATA SPECIFICATIONS & FILE FORMATS
Static “WebGIS”
Raster OGC WMS


GIF, JPG, PNG, (Geo)TIFF
OGC WFS, GeoJSON, TopoJSON, KML, SVG


XML, SHP, JSON
Vector
TMS, WMTS, XYZ, UTFGrid


GIF, PNG, JSON
Mapbox Vector Tile Specification


JSON, PBF
Tiled “Slippy”

Big Data Here: The Code

So Big Data Here, a little pop-up exhibition of hyperlocal data, has just closed, having run continuously from Tuesday evening to this morning, as part of Big Data Week. We had many people peering through the windows of the characterful North Lodge building beside UCL’s main entrance on Gower Street, particularly during the evening rush hour, when the main projection was obvious through the windows in the dark, and some interested visitors were also able to come inside the room itself and take a closer look during our open sessions on Wednesday, Thursday and Friday afternoons.

Thanks to the Centre for Advanced Spatial Analysis (CASA) for loaning the special floor-mounted projector and the iPad Wall, the Consumer Data Research Centre (CDRC) for arranging for the exhibition with UCL Events, Steven Gray for helping with the configuration and setup of the iPad Wall, Bala Soundararaj for creating visuals of footfall data for 4 of the 12 iPad Wall panels, Jeff for logistics help, Navta for publicity and Wen, Tian, Roberto, Bala and Sarah for helping with the open sessions and logistics.

The exhibition website is here.

I created three custom local data visualisations for the big screen that was the main exhibit in the pop-up. Each of these was shown for around 24 hours, but you can relive the experience on the comfort of your own computer:

bdh_buses

1. Arrival Board

View / Code

This was shown from Tuesday until Wednesday evening, and consisted of a live souped-up “countdown” board for the bus stop outside, alongside one for Euston Square tube station just up the road. Both bus stops and tube stations in London have predicted arrival information supplied by TfL through a “push” API. My code was based on a nice bit of sample code from GitHub, created by one of TfL’s developers. You can see the Arrival Board here or Download the code on Github. This is a slightly enhanced version that includes additional information (e.g. bus registration numbers) that I had to hide due to space constraints, during the exhibition.

Customisation: Note that you need to specify a Naptan ID on the URL to show your bus stop or tube station of choice. To find it out, go here, click “Buses” or “Tube…”, then select your route/line, then the stop/station. Once you are viewing the individual stop page, note the Naptan ID forms part of the URL – copy it and paste it into the Arrival Board URL. For example, the Naptan ID for this page is 940GZZLUBSC, so your Arrival Baord URL needs to be this.

bdh_traffic2

2. Traffic Cameras

View / Code

This was shown from Wednesday evening until Friday morning, and consisted of a looping video feed from the TfL traffic camera positioned right outside the North Lodge. The feed is a 10 second loop and is updated every five minutes. The exhibition version then had 12 other feeds, surrounding the main one and representing the nearest camera in each direction. The code is a slightly modified version of the London Panopticon which you can also get the code for on Github.

Customisation: You can specify a custom location by adding ?lat=X&lon=Y to the URL, using decimal coordinates – find these out from OpenStreetMap. (N.B. TfL has recently changed the way it makes available the list of traffic cameras, so the list used by London Panopticon may not be completely up-to-date.)

bdh_census

3. Census Numbers

View / Code

Finally, the screen showed randomly chosen statistical numbers, for the local Bloomsbury ward that UCL is in, from the 2011 Census. Again, you can see it in action here (wait 10 seconds for each change, or refresh), and download the code from GitHub.

Customisation: This one needs a file for each area it is used in and unfortunately I have, for now, only produced one for Bloomsbury. The data originally came, via the NOMIS download service, from the Office for National Statistics and is Crown Copyright.

bdh_traffic3

Big Data Here

9k-1

The Consumer Data Research Centre (CDRC) at UCL is organising a short pop-up exhibition on hyperlocal data: Big Data Here. The exhibition is taking place in North Lodge, the small building right beside UCL’s main entrance. The exhibition materials are supplied by the Centre for Advanced Spatial Analysis (CASA).

Inside, a big projection shows local digital information. What the screen shows will change daily between now and Friday, when the exhibition closes. Today it is showing a live to-the-second feed of bus arrivals at the bus stop outside the North Lodge, and tube train arrivals at Euston Square station just up the road. Watch the buses zip by as they flash up “Due” in big letters on the feed. Both of these are powered by Transport for London’s Unified Push API, and we are planning on publishing the visualisation online next week. Tomorrow will be showing a different local data feed, and then a final one on Friday.

cvimcbqwgaa4bkw

Opposite the projection is the iPad Wall. This was created by CASA a few years back by mounting a bank of iPads to a solid panel (above photo shows them in test mode) and allowing remote configuration and display. The wall has been adapted to show a number of metrics across its 12 panels. Four of these showcase footfall data collected by one of our data partners, and being used currently in CDRC Ph.D. research. The other panels show a mixture of air quality/pollutant measures, tube train numbers and trends, and traffic camera videos.

We hope that passersby will enjoy the exhibition visuals and use them to connect the real world with the digital space, a transposition of a digital data view onto the physical street space outside.

The exhibition runs 24 hours a day until Friday evening, with the doors open from noon until 3:30pm each day. The rest of the time, the visualisations will be visible through the North Lodge’s four windows. The exhibition is best viewed at night, where the data shines out of the window, spilling out onto the pavement and public space beyond:

2q

Big Data Here is taking place during Big Data Week 2016. Visit the exhibition website or just pop by UCL before Friday evening.

9k

cvmvee-xyaaxmep

Busiest Tube Station Times

chesham_max

Here are the busiest Tube station quarter-hour periods, based on the Transport for London 2015 RODS data (modelled, based on typical autumn weekday), used in Tube Heartbeat, adding together entries, exits and interchange stats and excluding Kensington Olympia which does not have a frequent Tube service.

The main pattern shows that stations further out (map) from London’s main work areas (The West End, the City and Canary Wharf) have an earlier morning peak (or later evening peak), due to the journey taking longer and the tendency for many people to arrive at their work-end station at about the same time – just before 9am. A secondary effect is that stations which just act as simple commuter home and work portals, we would expect the usage to peak in the morning rush hour, rather than than evening one, as the morning rush hour is shorter and so the simple commuter flow is more concentrated. Therefore, stations which show a peak in the evening are often due to a combination of this simple commuter flow and an evening “going out” destination.

Station Peaks by Time of Day

7:15am-7:30am: Chesham [Zone 9]

7:30am-7:45am: Chalfont & Latimer [8], Epping [6]

7:45am-8:00am: Amersham [9], Chorleywood [7], Debden [6], Elm Park [6], Hillingdon [6], Hornchurch [6], Theydon Bois [6], Cockfosters [5], Pinner [5], South Ruislip [5], Stanmore [5], Mill Hill East [4], Chigwell [4], Grange Hill [4], Perivale [4],Kew Gardens [3/4], Wimbledon Park [3], Holland Park [2]

8:00am-8:15am: Alperton, Arnos Grove, Balham, Barking, Barkingside, Becontree, Buckhurst Hill, Canons Park, Chiswick Park, Clapham South, Colindale, Colliers Wood, Croxley, Dagenham East, Dagenham Heathway, Eastcote, East Putney, Edgware, Fairlop, Finchley Central, Gants Hill, Hainault, Harlesden, Harrow-on-the-Hill, Hatton Cross, High Barnet, Hounslow Central, Hounslow East, Hounslow West, Ickenham, Kenton, Kingsbury, Loughton, Moor Park, Morden, Neasden, Newbury Park, Northfields, North Harrow, Northolt, Northwick Park, Northwood, Northwood Hills, Oakwood, Osterley, Parsons Green, Preston Road, Ravenscourt Park, Rayners Lane, Redbridge, Rickmansworth, Roding Valley, Ruislip, Ruislip Gardens, Ruislip Manor, Seven Sisters, Snaresbrook, South Ealing, Southfields, Southgate, South Harrow, South Kenton, South Wimbledon, Stamford Brook, Sudbury Hill, Sudbury Town, Totteridge & Whetstone, Turnham Green, Upminster Bridge, Upney, Wanstead, Watford, West Acton, West Harrow, West Ruislip, Wimbledon, Woodford, Woodside Park

8:15am-8:30am: Acton Town, Archway, Arsenal, Blackhorse Road, Boston Manor, Bounds Green, Bow Road, Brent Cross, Brixton, Bromley-by-Bow, Burnt Oak, Canada Water, Canning Town, Dollis Hill, Ealing Broadway, Ealing Common, East Acton, East Finchley, Finchley Road, Finsbury Park, Fulham Broadway, Golders Green, Goldhawk Road, Hammersmith (H&C), Harrow & Wealdstone, Hendon Central, Highgate, Kensal Green, Kilburn, Kilburn Park, Leytonstone, Maida Vale, Manor House, North Acton, North Wembley, Park Royal, Plaistow, Putney Bridge, Queen’s Park, Shepherd’s Bush Market, St. John’s Wood, South Woodford, Swiss Cottage, Tooting Bec, Tooting Broadway, Tottenham Hale, Tufnell Park, Upton Park, Walthamstow Central, Warwick Avenue, Wembley Park, West Brompton, West Finchley, West Hampstead, Willesden Green, Wood Green

8:30am-8:45am: Baker Street, Bank/Monument, Barons Court, Belsize Park, Bermondsey, Caledonian Road, Canary Wharf, Chalk Farm, Earl’s Court, Edgware Road, Elephant & Castle, Euston, Hammersmith, Hampstead, Highbury & Islington, Holloway Road, Kennington, Kentish Town, Ladbroke Grove, Lancaster Gate, London Bridge, Marylebone, Mile End, Moorgate, Notting Hill Gate, Oval, Paddington, Pimlico, Richmond, Royal Oak, Stepney Green, Stockwell, Uxbridge, Vauxhall, Victoria, Westbourne Park, West Kensington, Westminster, Whitechapel

8:45am-9:00am: Barbican, Aldgate East, Blackfriars, Borough, Cannon Street, Chancery Lane, Edgware Road (Bakerloo), Euston Square, Farringdon, Great Portland Street, Latimer Road, Mansion House, Old Street, Regent’s Park, Southwark, St. James’s Park, St. Paul’s, Warren Street

3:30pm-3:45pm: North Ealing

5:00pm-5:15pm: Heathrow Terminal 5

5:15pm-5:30pm: Willesden Junction

5:30pm-5:45pm: Aldgate, Russell Square, South Kensington, West Ham, Heathrow Terminals 1 2 3, Heathrow Terminal 4

5:45pm-6:00pm: Bond Street, Embankment, Goodge Street, Green Park, Gunnersbury, Hanger Lane, Wood Lane, Holborn, King’s Cross St. Pancras, Knightsbridge, Lambeth North, Liverpool Street, Mornington Crescent, North Greenwich, Oxford Circus, Stonebridge Park, Charing Cross, Stratford, Temple, Tower Hill, Turnpike Lane, Upminster, Waterloo, White City

6:00pm-6:15pm: Angel, Camden Town, Covent Garden, East Ham, Gloucester Road, Greenford, High Street Kensington, Hyde Park Corner, Leicester Square, Leyton, Marble Arch, Piccadilly Circus, Queensway, Shepherd’s Bush, Sloane Square, Tottenham Court Road

6:15pm-6:30pm: Bayswater [1], Bethnal Green [2], Clapham Common [2], Clapham North [2], Queensbury [4], Wembley Central [4]

You can explore graphs of the flows, in detail, at Tube Heartbeat – just choose the station of your choice on the drop-down on the top right, or click on it on the map.

Six Rush Hours?

Interestingly, if you look at the flows between stations, you can actually see SIX rush hours each weekday (you can see five of them below by looking across these sample segment graphs):

fiverushhours

These are:

  • A early morning peak, 7-8am. This is distinct from the main morning peak, and can be seen certain segments in east London, particularly on the District line near Plaistow, where the two morning peaks are an hour apart, with a noticeable dip in flow between the two. This may reflect the workforce for some traditional industries with 8am-4pm historical or shift-based working hours.
  • The main morning rush hour that almost all stations and line segments see – 7:30am-9am. Some of the more outlying stations (Zones 5-9) see their peak for this rush hour earlier than 8am, as it takes a while to get into the centre of London. You can see this is not the 7-8am peak above, by “tracing” the ripple through the network towards central London.
  • School home-time at roughly 3-4pm. Mainly affects some smaller, outer London stations, particularly in the north-west, for example Moor Park.
  • A corresponding 4-5pm peak for shift workers who started at 8am. Only a few links show this, such as Wembley Central in north-west London. The evening rush hours are less “compressed” than the morning ones so it is generally harder to distinguish between this one and the next one.
  • The main evening rush hour, 5-7pm.
  • Theatreland end-of-show rush hour, 10-11pm. Noticeable around Leicester Square, Covent Garden and Holborn. Some other areas, with established night-time economies, may also see a slight peak around this time.

You can also see 3+ rush hours in some of the stations, such as Wembley Central, which shows all six:

wembley_max

Mapping at the Edge – the BCS/SoC Conference 2016

bcsconf_osquarterinch

The British Cartography Society and Society of Cartographers* once again combined their two annual conferences together, for a two-day meet in Cheltenham in early September. After last year’s win for the DataShine website, I was there in a more passive capability, although my colleague Dr Cheshire, who collected a trio of prizes last year, presented on why cartographers should learn to code too – see his talk summary.

I like the BCS conference format – it’s quite a small conference, so there’s only two streams, unlike many trade-focused conferences the trade exhibits don’t dominate the space, instead the talks themselves are the main focus. The residential nature of the conference also promotes a relaxed feel. Stand-out talks for me included Ross McDonald‘s excellent summary of new features in QGIS 2.14, Dr Cheshire’s talk about cartographers needing to code, and finally a walkthrough on creating an impressive relief map using Blender 3D, from Steven Bernard of the FT.

A highlight was the awards ceremony – and not just because of last year’s win. Every Person in Scotland Mapped won the Ordnance Survey Open Data award. This simple but effective visualisation assigns each person in Scotland to a dot in a housing block, as represented in OS OpenMap Local, filtering out non-residential buildings. It combines population density information from the census, with area information for each block.

bcsconf_awards

There is an accompanying exhibition, showing the various entrants for the awards – this is a highlight for me, because it’s great to see many novel printed maps in the same place, many showing innovative ideas. You can’t beat a good large-print map. I particularly liked the GIS-powered reimagining of two classic Ordnance Survey mapping styles with modern datasets, by Charley Glynn of the OS – there’s a 19th century London style brought up to date, but my favourite is a reworking of the 1960s OS “Quarter Inch” style with the strong colours for mountains. It always looked good in Scotland, and Charley has produced a version with modern data for the West Highlands – see the extract above. You can buy it from the OS shop online.

I would love to see this idea expanded to cover the whole of the UK – a key of course is to have it all automatically generated. The human cartographer’s input is still required for label positioning etc – the “last 10%” of the effort is still manual.

I also liked the pop-up trig point at the OS stand:

bcsconf_ostrig

* It is a curiosity that there are two national bodies representing cartographers in the UK, especially considering that the field is quite small anyway. The BCS is larger and more industry focused, while SoC is smaller and more academia focused. It’s great to see both bodies coordinating their annual conference to be the same event, as happened last year and this year – long may it continue.