Eight Ways to Better Flow Maps

As part of a presentation I gave yesterday at the RSAI-BIS (Regional Science Association International – British & Irish Section) annual conference, on DataShine Travel to Work maps, I outlined the following eight techniques to avoid swamping origin/destination (aka flow) maps with masses of data, typically shown as straight lines between each pair of locations.

Lines tend to obscure other lines, making the flows of interest and significance harder to spot, and creating an ugly visual impact. See above for an extreme example which shows (all) cycle-to-work flows in inner-city London. Large numbers of flow lines, if delivered as vectors to a web browser, can also cause the web browser to run slowly or run out of memory, affecting the user experience.

To avoid this, I generally try to use one or several of the following techniques.

1. Restrict to a single origin or a single destination. This does require the user to first click on a location of interest before any flow can be seen:

From L to R, DataShine Commute, Understanding Scotland’s Places (USP) and DataShine Region Commute, the last one showing that, in some cases, this can still produce an overload of lines.

2. Only show flows above a threshold. This could be a simple minimum value threshold (e.g. 10 people), a set number of lines (e.g. 1000 largest flows) or dynamic value-based limit (e.g. only where flow is 1% of the origin population), the latter generally only working if a single origin is shown at a time:

From L to R, The Great British Bike To Work (with a simple flow-size threshold) and Understanding Scotland’s Places, which uses a dynamic origin-based theshold, shown here with the constrasting number of bidirectional flows visualised from a large city (centre) with those from a small town (right), each being selected in turn.

3. Minimise the overall number of possible origins/destinations. What you lose in detail you might gain in clarity and simplicity. DataShine Region Commute only shows flows between LAs, rather than the spatial detail of flows within them.

4. Restrict the geography. The Propensity to Cycle Tool (Lovelace R et al, 2017) shows the main flows (based on a threshold) on a county-by-county basis, with easy and clear prompts to allow the user to move to a neighbouring county if they wish.

5. Bend the lines. Tools, such as the Stanford Flow Map Layout tool or Gephi with the “Geo Layout” and curved lines, allow flow lines to be clustered or curved in a way that reduces clutter, while retaining geography. The first approach clumps pairs of flow lines together in a logical way, as soon as they approach each other. The second approach simply curves all the lines, on a clockwise basis, generally removing them from the central area unless that is their destination. See also this paper by Bernhard Jenny (Jenny B. et al, 2017) which details the benefits of curving lines and further cartographic modifications, and this paper by Stefan Hennemann (Hennemann S. et al, 2015) which outlines a sophisticated approach to grouping together flow lines, on a world-wide basis.

From L to R: Commutes into London from districts outside London, from the 2001 census, by Alastair Rae (Rae A., 2010) using the Stanford Flow Map Layout tool, and top destination for each origin tube station, based on Oyster card data, by Ed Manley (Manley E., 2014) using a particular Gephi flow layout.

6. Route the flow. Snap the lines to roads or other appropriate linear infrastructure, using shortest-path or sensible-path routing, and combining the segments of lines that meet together, either by increasing the width or adjusting the hue or translucency.

From L to R: The Propensity to Cycle Tool (Lovelace R et al, 2017) routed for shortest path, and journeys on the “Boris Bikes” bikeshare system in central London, routed with OSM data to the shortest cycle-friendly route. In both cases, journeys meeting along a segment cause the segment to widen proportionally.

7. Don’t use a simple geographical map. This map, created by Robert Radburn at City University (Radburn R, 2015) in Tableau, is a “small multiple” style map of car commutes between London boroughs, with a map of London being made up itself of miniature maps of London. Each inner map shows journeys originating from the highlighted borough to the other boroughs. These maps are then arranged in a map themselves. It takes a little getting used to but is an effective way to show all the flows at once, without any potentially overlapping lines.

8. Miss out the flow lines altogether. Here, a selected origin (in green) causes the destination circles to change in size and colour, depending on the flow to them. In this case, the flow is modelled commutes on the London Underground network – made clearer by the addition of the tube lines themselves on the second map – but just as a background augmentation rather than flow lines.

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Dockless Bikeshare in London – oBike is Here

London has a new bikeshare system – and it’s appeared by surprise, overnight. oBike is a dockless bikeshare. The company is based in Singapore, where it runs a number of large dockless systems there and in various Chinese cities, Melbourne, Amsterdam and Zurich, it is also likely coming to Washington DC in the USA and to Berlin in Germany, based on some recent job postings.

And now they’ve shipped 29 lorry-loads of nearly 5000 bicycles to London, the number being revealed in a now-deleted tweet by a logistics company:

Just under 1500 have been released so far, initially being “seeded” in groups along the major roads in Tower Hamlets and Hammersmith & Fulham boroughs (400 in each), and more recently in Wandworth, Clapham, Kennington, Lewisham, Waterloo, Harrow* and Enfield*, with “organic” use moving the bikes out as far south as Kingston, and as far east as East Ham (plus possibly in the river near Erith…) There have been several hundred journeys already, with the great majority of bikes having been moved at least once from their initial deployment.

Other players in the space are MoBike (in Manchester), OfoBike (in Cambridge – N.B. website currently down) and YoBike (in Bristol). Another company, GetBike, claimed to have launched in London a few months ago but the bikes, to date, have not appeared. Possibly, they got mired in council discussions. MoBike is also launching a system in Ealing, west London, at the end of the month. All five companies are based in Asia, where mass cycle manufacture is cheap, which had led some cities there ending up with huge heaps of dockless bikeshare bikes, being piled up by desperate city councils trying to keep their pavements clear.

As the bike is the only physical presence on the street, there are no permanent structures for the system and so authorities are not always involved in the process, but have to pick up the pieces and clear the streets – leading some in the dock-based bikeshare establishment to term the systems as rogue bikeshares. The European Cyclists Federation have this week published this timely position paper, where they term the systems slightly more politely as “unlicenced bikeshare” and suggest a potential framework to make the concept work in a European urban context. Whether the operators take notice of course is another matter…

Meanwhile, oBike’s rollout continues. In the map below, red dots with yellow borders show the most recently organically moved bikes (i.e. areas of red/yellow = popular use) while the blue dots with turquoise borders show ones which have not moved since their initial deployment. The other bikes (which someone has moved, but not recently) are shown with purple dots. The map is just a snapshot, and is manually created by myself, so I may have missed some bikes (but I think I’ve got almost all of them):

So far, most of the rollout has been to areas already served by bikeshare – the Boris Bikes (aka Santander Cycles). The real value add for London will be when Zones 3-6 (i.e. non-tourist, non-hipster “real London”) get the bikeshare. After 7 years of the Boris Bikes and no sign of them extending outwards, it’s about time the rest of us got the value of bikeshare too, particularly as our alternative options are more limited.

What is it?

Dockless bikeshare is different from the so-called “third generation” dock-based systems like London’s existing Santander Bicycles or “Boris Bikes”. It does away with docking stations and credit card terminals for charging and storing the bikes and administering the access, instead the bikes themselves have locks which contain a solar panel GPS receiver and SIM card for broadcasting their location, and are controlled by an app on your smartphone. It massively cuts down on the costs of the system because no docking stations are needed. London’s docking stations are very expensive as they have to go through the planning process, and also need to be wired up for power. There are also fewer staff needed – oBike do not employ drivers to redistribute the bikes, and also don’t have an established call-centre. Payments are handled entirely through the app. Maintenance teams are also, I suspect, likely to be minimal on the ground.

The bikes themselves are similar in size to the Boris Bikes, but come with solid rubber tyres (so no punctures). They feel around the same weight. The bikes only have only one gear, set quite low, so you can’t get up much speed. The bikes don’t feel heavier than a Boris Bike. They have the same, chunky “tank” feel to them and feel sturdy – the livery being bright yellow helps with visibility on the streets, which is a bonus.

Trying it Out

I took an oBike out for a spin yesterday afternoon. I noticed a pair parked (orange pins) close to the Facebook office at Euston Square – maybe some Facebookers trying out the latest thing?

On arrival, I was a little surprised to see the bikes were parked on the other side of the road (small blue pin). Still, my own phone’s GPS was saying I was on the other side of a large building (blue dot)…

The process of getting the bike was straightforward – I had already paid the £29 refundable deposit (£49 from August) by entering my card details into the app on my phone, so it was just a case of clicking “Unlock” and the scanning the QR code on the bike’s stem. Around 10 seconds later (with communication through your phone’s Bluetooth or through the SIM card on the lock – I’m not sure) the lock on the bike clicked open and the app’s timer started. Neat! The whole signup process was far more streamlined than with the Boris Bikes, where you have to go to a docking station, use the terminal there, page through tens of screens of information and put in your credit card at least twice. Here, you can be on board in less than a minute, with subsequent hires even quicker.

My bike already had its seat raised to the highest position (or higher still, as there was some brown scuffing there) so no adjustments needed. I headed across Euston Circus and down to the British Museum. Unfortunately, my bike had a distinct squeak every time the back wheel rotated, although squeezing the brake stopped it for a few seconds. The brakes themselves are excellent (perhaps I noticed this particularly as my own bike brakes are poor) and everything seemed OK. The bike appeared in good condition, no rubbish had collected in the basket. The handlebars are very wide, so I couldn’t squeeze through the usual gaps between cars and buses. I didn’t find the handlebars very grippy – they are plastic rather than rubber, and my left hand slipped off at one point (I was juggling a mobile phone at the time). In all, not the fastest cycle but perfect fine for utility riding and definitely still faster than walking or getting the bus.

There were various Boris Bikers around, I must have passed at least 10 in my 15 minute ride, but no other oBikes – yet! At the end of my journey I dropped the bike beside a bike rack beside Euston Square station. It wasn’t immediately obvious how to end the journey – you don’t press anything in the app, instead you pull the lock switch manually back across the back wheel, until you hear a reassuring click. A few seconds later, as long as you have Bluetooth switch on, on your phone, then the app beeps and confirms the journey as complete.

On finishing, the app presents an attractive display showing your start and finish, time, and a “route”, however the route is simple the Google Maps route for bicycles rather than the actual journey taken. The distance also bears no correlation to either the Google Maps “shortest path” route on the map, or the actual distance taken, which is very odd. For the finish location itself, the GPS had once again not given a particularly accurate result, and it looked like I’d cycled the bike straight into the A&E department at University College Hospital. Only 100m or so off again, but not ideal for discovery:

I tried another bike out a bit later. This didn’t have a squeak, however the basket was tilted slightly to one side – not a biggie but still a bit worrying that quirks like this are appearing so soon into the deployment. The bike coped just fine with the rough surface on the River Lea towpath, including over several speedbumps. However, on a return journey, the unlocking process proved to be rather fraught. The QR code was read fine by my phone, but the communication to the lock was not working well, and it kept timing out. Only after around 6 attempts, including moving the bike around. The area we were in had quite poor mobile reception so this may be part of the problem. Still, the few minutes delay to the journey was frustrating. However, once we were moving, the bike itself performed well.


I really like the app, and the payment structure is excellent – 50p flat rate per 30 minutes represents much better value than the £2/day for 30-minute-max journeys on the Boris Bikes. I really didn’t like that, before oBike, it was cheaper to get a bus than a bicycle in London. The reward system is a great idea, it always made sense to incentivise riders to do the tasks of the operator, and the lack of redistribution is another good thing – I always thought it was a huge waste of time redistributing bicycles to one place, only to redistribute them back later.

The fact that, rather being constrained to docks, the allowed operating area is the whole of London, is great. Already, one bike has ended up at Heathrow in the far west of London:

The lack of docks mean that the users set the area of coverage. Finally, Hackney and Haringey, Lewisham and Rotherhithe, have the bikeshare that I am sure would always have been popular there.

I do also like the name – oBike (five letters, two syllables) rolls off the tongue a lot more easily than Barclays Cycle Hire or Santander Cycles.


I’m not totally convinced that oBike will survive long-term – despite assertions to the contrary by some operators of these dockless bikeshares, the bikes will need maintenance due to the rough weather, roads and people in London. Whether they get any will be interesting to see. The bikes I tried out have only been on the streets for four days, and for the first one I tried have picked up a loud squeak so quickly, and the second one to have a wonky basket, is not great. The bikes are also a little too small for the British frame – having said that I am 6′ and I got around OK on one, but with the seat-post extended to its absolute maximum. There have been some cases of the seat-posts easily coming right off when extended further.

Also – some people will inevitably be hard on the bikes. They are not that indestructible, and some people will see them as a cheap way of getting a bike. Some will end up in the Thames. Councils will end up confiscating some, as Hammersmith & Fulham has already threatened to do. Not getting the council involved is brave – they may have looked at the Cambridge example where the council insisted that another operator reduce its launch from 500 bikes to 20 – there are obvious advantages with not having to deal with 33 separate councils in London (+ the City + TfL and the GLA) and just sticking the bikes out there – but oBike could have made life easier for themselves by distributing them more discretely, to avoid the ire of grumpy councils and pedestrians – placing one or two bikes together, at the most, ad on side roads rather than main roads, beside existing cycle parking racks, rather than obstructing pavements, and focusing on Zones 3-6 first (even if that results in a slower initial takeup). And a commitment to maintenance or organised disposal would also be good – at the moment no one knows what will happen to the bikes after they start to wear out. The scenes of “bicycle graveyards” and huge heaps of brightly coloured bicycles, in the cities of the far east that are full of dockless bikeshares, are worrying.

I hope that oBike is a success, and the bicycles survive grumpy councils, the kids who just want a free bike, and the weather. If it provides an incentive to give the Boris Bikes a kick up the backside (50p per 30 minutes flat rate and all-London low density coverage please!) then that on its own would be a result. Providing a bikeshare out into London’s Zone 3 and further is a real winner for shared mobility options outside London’s already well connected central core.

* These have since been removed, I understand, following discussions with the councils there. The company however did not switch off the GPS trackers on the removed bikes, and so have revealed the location of their depot, at Rainham on the very edge of east London:

Map background Copyright HERE Maps. Top photo Copyright JC, bottom photo Copyright SR.

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Broadband Speed in the UK

Recently published on CDRC Maps is a new a map of Broadband Speed in the UK. This is the average download speed for residential properties, right across the UK. It’s based on data annually released by the national regulator, OFCOM (I’m using the most recent dataset, from 2016). I’m using a Purple-White-Green colour ramp, where purples indicate areas with very slow speeds, white tends towards the national median and dark greens show areas of very fast connection – potentially homes using the new “ultrafast” connections available in some areas.

It should be noted that this is based on the actual average download speed based on the deal people have signed up for, not the maximum attainable download speed (either theoretical or actual) in an area. I hypothesise below that, in cities, this may be due to consumer inertia as much as infrastructure gaps – while in rural areas it is more likely the latter. I’m also only mapping residential speeds, so ignore the map shades on commercial buildings – the values there refer to nearby residential, and also broadband through high-speed mobile networks rather than “fixed line” is also excluded.

Urban/rural divide

As would be expected with infrastructure costs, the economics of putting in fibre connections, and increased distances to the nearest telephone exchanges, broadband speeds still suffer in the countryside, with the Llandrindod Wells (LD) postal area in rural central Wales, having the slowest average broadband connection of 14.9Mbit/s. Looking at specific postal outcodes, PA70, on the also extremely rural island of Mull in western Scotland, has an average speed of just 1.1Mbit/s.

Why do city centres show up as slow?

Of note, as well as this urban/rural divide, the very centre of cities often show slower speeds than the suburbs. This is possibly because of the difficulty of installing the needed infrastructure under narrow, busy streets and through old, often historic buildings. By contrast, newer housing developments, normally on the edge of cities may come with broadband infra designed in to the plans. The fastest postal region is OX, the Oxford postal area, perhaps reflecting the large technologically literate population (thanks to the universities and various science parks in the area). The fastest postal outcode in the country, however, is N1C, the new area behind King’s Cross. This is a central city area, but one which has essentially been built from scratch in the last few years, rather than needing broadband retrofitted into it. Another new area however, E20 (the Olympic Park) appears in the London bottom 10.

An alternative argument is that it may be that city centres got the “first wave” of broadband capabilities, many years ago, and people switched then – and consumer inertia means that they are less likely to switch to faster broadband offerings that are now available to them. In central London, the Rotherhithe area shows up as having particularly slow broadband speeds being used. This area is quite distinct to just about every other central London area, having become a residential area in the 1980s and 1990s. It is also rather isolated geographically. However, the lowest speeds of all in London are found, rather surprisingly, in and around the few residential areas of the City of London. The Barbican Estate has few keen users of ultra-fast broadband. It may be available to them, but the elderly population here may just not want it.

A short note on methodology: This is an area average (by output area – 150 properties) of postcode averages of individual connections. I’ve excluded postcodes with no residential broadband connections, as these are still recorded in the source data but with a speed of 0. By using OAs rather than individual postcodes, the data is slightly smoothed, i.e. less noisy, so trends can be seen easily across areas, even though individual properties (or indeed whole postcodes) may be connecting at a faster speed than what appears in the map in that place. In short – the map is of the overall picture, not individual addresses.

You can download the data, and see the Top/bottom 10 postal area stats, on the CDRC Data page for the dataset, or explore the data on the interactive map.

Top: A river divides them – broadband average download speed in west Glasgow. Above: Towns north and south of the Firth of Clyde. Below: Variations in south London. All maps based on data which is Crown Copyright OS and OFCOM.

Panama’s Population Geographies

Panama is a Central American country with around 4 million population. The country is split into 10 provinces (including one that was split from another in 2014). The population is obliged to register for and obtain an ID card, or “cedula” which contains an interesting attribute. The prefix of their ID number indicates their province of birth. This not only allows the mapping and analysis of surname (and other) demographic information across the country, but also, if combined with information on current location, even allows for a rudimentary analysis of internal migration in the country.

This official document contains lots of useful information. Subsequent to this, the “Panama” province within the country has split into two, with the westernmost section becoming Panama West and gaining a new province number 13. In practice, the great majority of people living here retain the prefix 8 as the population with “13-” prefixes will be too young to have appeared on school attendance lists, jury service lists, exam candidate lists or government worker salary transparency lists. Here is the very No. 13: Ashly Ríos, getting the number 13-1-001. (People are required to obtain their number by the age of 18 but you can be registered at birth.)

For most people, born in Panama, their cedula number prefix indicates the following provinces of birth:

Province Cedula prefix
Bocas del Toro 1
Coclé 2
Colón 3
Chiriquí 4
Darién 5
Herrera 6
Los Santos 7
Panamá 8
Panamá Ouest 13
8 (pre-2014)
Veraguas 9
Guna Yala (indigenous province) 10
3SB (pre-1953)
Madungandí (indigenous sub-province) 10*
8PI (pre-2014)
Wargandi (indigenous sub-province) 10*
5PI (pre-2014)
Emberá Wounnan (indigenous province) 11
5PI (pre-1983)
Ngäbe-Buglé (indigenous province) 12
1PI, 4PI or 9PI (pre-1997)

* These were briefly assigned No. 13, before being changed to 10.

The format of the cedula number is generally X-YYY-ZZZZ where X is the province number, YYY is the registry book number and ZZZZ is the number within the book. However, for certain groups, the prefix is different. If SB appears after the province prefix, this is an indication that the person was born in Guna Yala (formerly called San Blas), but before it became a standalone indigenous province. Other indigenous areas, some of which have not formally become provinces, were indicated by PI appearing after the prefix of the former or enclosing province, or AV if very old (born pre-1914). However, the numerical codes are now used.

Panamanians born outside the country get “PE” as their prefix instead. Foreigners are assigned “EE” while they retain their immigrant status. If they gain permanent residence rights, they are assigned “E”, and if they become full Panamanian citizens, they are assigned “N”. PE, N, E and EE do not officially have an associated province prefix, although one is occasionally added in third-party lists, or “00”. So, these people can also be assigned a separate ID, starting with “NT” and with an associated province prefix, this is a temporary ID issued for tax purposes, rather than a full cedula number.

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.

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.

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


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.


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.


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.


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.

< < < 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


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


D3 DataMaps

Leaflet for R/RStudio


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.

Static “WebGIS”
Raster OGC WMS



Mapbox Vector Tile Specification

Tiled “Slippy”