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Archive for the ‘Open data’ Category

Our talk at State of the Map 2019: Is the OSM data model creaking?

Sunday, September 22nd, 2019

We gave a talk at the State of the Map 2019 conference, the main annual meeting for OpenStreetMap, whose data we use for our cycle routing.

Our talk was entitled “Is the OSM data model creaking?”.

A video of the talk is now online:

Here are the slides from the talk:

Is the OSM data model creaking? (PDF, 16MB)

OpenStreetMap was designed to enable ordinary people to create open geodata that anyone can use and maintain easily. Traditional GIS concepts such as layers are dispensed with in order to make editing simple and accessible. In the same way that the web would never have taken off if HTML were not so accessible and tolerant of mistakes, this simplicity in OSM has meant a low barrier to involvement.

However, as OSM is becoming more widely used in the mainstream, the need for accuracy and quality is becoming more and more important. Cyclists need detailed turn data to enable high-quality routing that takes full account of safety. Satnav companies, need lane data, which is difficult to represent accurately. Pedestrian routing is barely in its infancy and high quality routing for people walking or using wheelchairs is hard to achieve.

At its root, OSM tries to represent spaces as flows (lines). This results in fundamental compromises and inaccuracies. What is good for routing is not always good for cartography, and vice-versa.

For instance, a street containing cycleways with pavements either side is usually represented as a single line with attributes. However, it is extremely challenging to represent properly all the parts of the street, and in general people simply don’t bother: a single line with large numbers of attributes is unwieldy to edit (even when hidden by editor GUIs), and just as challenging for a router to interpret. Continual changes in the width of a street cannot be easily represented without segmenting the street heavily and creating a mess. Temporary disappearance of lanes makes editing complex. Routing ultimately ends up as a lowest common denominator result.

The alternative method of representing this same street is as a series of individual lines. But this is equally problematic. In this model, the street loses its coherence as a single entity – humans think of it as a street with multiple uses (walking, cycling, driving, trees). Where people have done this, attributes such as street names need to be kept in sync, and in practice separate pavements often fail to have names attached. Concepts such as the ability to cross from one side of the road to the other (or even switch lanes) are not modelled, with the result that a router may take the user to the end of the street then back down. And cartography ends up showing a series of parallel lines which looks messy and does not match the human perception of a street.

The bicycle tagging page on OSM provides a perfect demonstration of the current problem: https://wiki.openstreetmap.org/wiki/Bicycle It shows the complexity of representing many common scenarios, with increasingly incomprehensible tagging combinations. No router implements anything like all of this, and even expert OSM contributors would shy away at bothering to add this data.

Cycleways indeed are a good general example of inconsistent tagging. Cycleways separate to a road are sometimes tagged as an attribute of the street and sometimes as a separate geometry. What about a hybrid/stepped cycle lane of the kind seen in Copenhagen – is that a cycle lane or a cycleway? Do lane counts include cycle lanes or not? How is obstructive car parking represented? Is the one-way indication applicable to the cycle lane on the road? And so on.

Another example is junctions. Should traffic signals be treated spatially (i.e. represent the location of the traffic heads), or should they be treated linearly so that routing works properly? How should the linear model work accurately when there is only a single geometry for multiple directions? Have a look at the roads around the Arc De Triomphe in Paris – it is completely impossible for a routing engine to work out exactly how many signal delays should actually be attributed based on the presence of the marking of traffic signals in the data: https://www.openstreetmap.org/#map=17/48.87391/2.29536

This talk will discuss these cases, and provide a starting point for discussion on what should be done to improve the situation. As people are ever keener to add more detail to the map, and as more and more mainstream users look to OSM, we have to ask whether the current model is arguably creaking too heavily. Is there a way that we can represent spaces as a set of interconnected flows in some way?

The speaker, Martin Lucas-Smith, is one of the developers of CycleStreets, one of the earliest and most established dedicated cycle routing engines. As such, he has spent many years considering the kinds of tradeoffs represented by the current OSM data model.

TfL Cycling Infrastructure Database

Friday, May 10th, 2019

TfL CID

Transport for London (TfL) have created a new database of cycling infrastructure, containing 240,000 assets, covering all of Greater London. This is proposed to be released as open data.

This groundbreaking database contains every cycle infrastructure asset within Greater London, including assets on and off-carriageway. The assets surveyed are: cycle parking; signals; signage; traffic calming measures; restricted points (e.g. steps); advanced stop lines; crossings; cycle lanes/tracks; and restricted routes (e.g. pedestrian only routes).

“The world’s first Cycling Infrastructure Database will be the most comprehensive database of cycling infrastructure ever collected in London. Over the past 18 months, TfL has amassed data on every street in London, cataloguing almost 146,000 cycle parking spaces, 2,000 km of cycle lanes and more than 58,000 cycle signs and street markings. This information will be released as open data alongside a new digital map of cycle routes, will make journey planning and cycle parking much easier, as well as offering valuable information to TfL and the boroughs for planning future investment in cycling.”

TfL is keen to make this available to the OpenStreetMap community under a compatible open license, to ensure maximum use of the CID. TfL is also potentially willing to consider tool development to help facilitate sensitive merging in of this data. OpenStreetMap is the street data on which CycleStreets is built, so the better data available to OSM, the better our routing can become.

Demonstrator map

We’ve created a demonstrator mapfor the purposes only of evaluation by the OSM community at this stage.

This demonstrator map contains only one of the 25 areas that have been surveyed.

We are specifically seeking comments on data quality and usefulness of this data from the OSM community. Initial analysis by CycleStreets is that the data is of excellent quality, and very suitable for conflation into OSM, to increase both comprehensiveness and metadata quality.

TfL CID

Usage notes: The controls on the right of the map allow the different feature types to be selected. The OSM layer (available at zoom level 19+) also provides a live feed from the OSM API, to enable quick comparisons. The two photos of each asset are in the process of being supplied; those already available and cleared in GDPR terms are included in the popup.

It is stressed that at this point, no permission is given for re-use of the data in any way, but TfL strongly intends to make this available in future. All 25 areas would be covered in the final data release, not merely the one shown currently in the demonstrator map.

Feedback

Feedback is very strongly encouraged, as soon as possible.

Please do discuss the data and related aspects noted above on the talk-gb mailing list.

Feedback and questions can also be e-mailed us.

We are happy to provide any clarifications, which will be added to this page, as a central repository of information about the project.

More detail

We’ve set up a new TfL CID project wiki page on the OpenStreetMap Wiki.

PhD studentship with University of Leeds: Towards data-driven policy development: the case of London’s built cycling infrastructure

Tuesday, May 8th, 2018

An exciting PhD studentship opportunity which we are involved in, cross-posted from the Data Analytics and Society Centre for Doctoral Training website. Deadline: 3rd June 2018.

Towards data-driven policy development: the case of London’s built cycling infrastructure

In 2013, £913m of funds was allocated over 10 years for investment in London’s cycling infrastructure. Much of this — including guided quietways, protected cycle superhighways and London’s crossrail for the bike — opened in summer 2016. The chief objective: to make cycling ‘a normal part of everyday life  […] something people hardly think about […and] something everyone feels comfortable doing’ (Greater London Authority 2013).

Traditionally, attempts to evaluate such interventions might rely on survey data describing changes in *claimed* behaviour or high-level data from Automatic Traffic Counters describing infrastructure occupancy. The former are often expensive to collect and suffer from numerous (well-documented) biases and the latter are too high-level to capture more subtle changes in behaviour.

This project will instead use new, large-scale observational datasets – from London’s bikeshare, underground and bus network, from route planning services (CycleStreets.net), user-contributed and social media data —  to describe changes in city-wide cycling behaviours pre- and post- the intervention. Crucially, it will identify rich detail around the impact of current investment on behaviour and contribute quantified estimates, under uncertainty, around the impact of future investment.

Applications are welcomed from those wishing to develop expertise in statistical model building, geospatial data and information visualisation.

Start Date: October 2018
Lead Supervisor
: Roger Beecham (University of Leeds)
Other Supervisors: Robert Aykroyd, Robin Lovelace, Stuart Barber
Partners: University of Leeds
External partners: CycleStreets.net

Read more and apply online here.

DfT cycling data for research/OpenStreetMap use – as GeoJSON

Monday, May 7th, 2018

A techie post, about some cycling data which may be of use to people researching cycling.

Back in 2011, we were involved in a project to convert some newly-collected cycling data from the UK’s Department for Transport for use in OpenStreetMap.

The data was originally collected for the DfT’s Transport Direct project (which has since ceased operation), which was an early attempt to create a government cycle journey planner, launching on almost the same day as CycleStreets itself. In order to ensure that taxpayer value for this expensive (£2.4m) dataset was not lost as Transport Direct fell into disuse, CycleStreets successfully encouraged the DfT to release the data openly and to do so in a way which would encourage use in OpenStreetMap. We helped with that process, and data for cities started to be merged in from 2011.

The data consists of the cycle network as of 2011 in each city of over 30,000 people. This does however not represent all cycle infrastructure – only where signage is present. Additionally, collection included the Sustrans NCN network.

Since that time, things have moved on. OpenStreetMap has become the go-to datasource for cycling data internationally. GeoJSON has become the de-facto format for open geographical data. Similarly, Github has become the de-facto location to distribute open data like this.

As part of a project we have been working on with Leeds University, the Cycling Infrastructure Prioritisation Toolkit (CyIPT) (which we will report on soon), we needed historical data from 2011, and this DfT data was a perfect candidate.

Accordingly, we have taken the opportunity to recover the data from 2011, and convert it to GeoJSON and republish it, in the hope it might be useful for some people. It is an OpenStreetMap-orientated version of the data published on data.gov.uk. As such it is subject to OSM licensing conditions.

DfT England Cycling Data 2011

We would stress that, for almost all uses, CycleStreets instead strongly recommends downloading data from OpenStreetMap, which is topographically routable and is maintained and has far greater geographical coverage. Moreover, the cycle network has changed from 2011-18. However, the data in this repository contains attributes on each geometry which remain often more detailed than OSM. Accordingly, the data is most useful for research purposes and for manual merging into OSM, which is encouraged.

Full details about the data are given in the README which can be found on the main repository page.

Coverage:

Example area – Cambridge:

Every GB road collision – mapped

Sunday, October 1st, 2017

As campaigners for getting more people cycling, a crucial issue for us is safety of our streets. Safer streets means more people cycling – as places like the Netherlands and other European cities show.

Every year, the Department for Transport issues a massive data release, detailing every reported road collision in Great Britain, what vehicles were involved, and the outcome in injury terms. Known as STATS19, the data contains around 60 pieces of information for every collision – whether slight, serious or fatal. This is excellent work by the DfT who collate this.

We’ve plotted every collision, and made available the full details, on a map on our new Bikedata website.

The data for 2016 has just been released – we got it online within a few hours of its release.

The site is a beta – the two things we want to improve are removing the jumpiness of the icons, and dealing with the question of how to show large numbers of collisions when zoomed out – currently a limit is applied. Zooming in shows all.

Since the data was first made live, we’ve fixed a few issues. The latitude/longitude values in the original data were incorrect, so we’ve reprojected these from the northings/eastings values which are the original data. Some data in London was also misnumbered, which the DfT have corrected after we pointed this out. Our interface also was not filtering correctly for car occupants – now fixed.

For every collision, you can click to get full details – available openly without charge.

 .  

Behind every one of these is a human story:

Users of the site are finding that the visual display enables patterns to be spotted – such as the way that the typical British roundabout design fails cyclists:

as do junctions more generally:

We’re now working to add new comparison facilities – we want to bring out the policy implications hidden in this data.

For instance, how do different Local Authorities compare? What happens when streets are upgraded to add safe, segregated infrastructure? How can we most easily demonstrate that allowing two-way cycling in one-way streets is a perfectly safe improvement.

We’ve also wanted for a long time to link these to newspaper reports and are considering methods to enable people to do that.

Let us know what you’d find useful.

Developers – need cycle parking in your app? Use our Cycle parking API

Tuesday, August 29th, 2017

This is a post for app developers. So it contains some techy stuff which probably won’t be of interest to our cycle routing users.

Knowing where you can find cycle parking as a cyclist, especially in cities, is helpful in avoiding theft, as well as helping keeping busy streets tidy.

By providing information on where cycle parking exists, developers of apps can easily help people find cycle parking before they even reach their destination.

CycleStreets provides a Cycle parking API as one of the points of interest (POI) types that can be retrieved in our extensive cycling API suite. Developers can embed this in our app – either by making realtime calls or obtaining the data en-batch using the CSV export mode.

You can see an example implementation on our new Bikedata website:

The API provides the following data:

  • Locations of all cycle parking in the UK and other areas that we support (much of northern Europe and various cities around the world – contact us if you need other areas)
  • Whether the parking is public or private
  • Number of bikes that can be parked (where data is available)
  • Details of whether the cycle parking is covered, what type of stands, etc. (where data is available)
  • The location of the entrance point rather than the centre point (for larger installations, where data available)
  • (Coming soon) Large areas of parking to be available as areas rather than (entrance) point

By default, all locations (whether public or private) come through, but you can specify a filtering option (as seen in the Bikedata example) if you wish.

We perform a range of pre-processing to enhance the raw data:

  • If the location is on private land, but the parking itself is not marked as such, we pre-process the data to mark it as private
  • For larger installations such as cycle parks, we use the entrance point as its location, rather than the centre-point
  • We convert data defined as either points or areas into a unified set of points

This data is all possible thanks to the power of OpenStreetMap. We regularly import OSM data, perform a range of processing on it (e.g. convert locations on private land into private POIs, determining entrance points, etc.), and turn this into an indexed API.

Our API is a flexible, robust and well-maintained interface (it has been running since 2010). If you need a Service Level Agreement, or are likely to require high volumes of requests, we can also provide the API on a contractual basis.

To get started, just obtain an API key, and set your application to make API calls to the POIs API and render the GeoJSON response on your map.

If you would like to request any enhancements to the API, to cover your use-case, do get in touch.

Also, if you have any existing cycle parking data, we are happy to provide advice on how to make it available.

Bikedata beta – data for cycle campaigners in one place

Sunday, June 11th, 2017

We’ve been working on a new website, Bikedata (working title), providing cycle campaigners around the UK with a ‘one-stop shop’ for data that helps them in their work.

Today we announce the open beta of the site – ready for you to try out, but we know there are bugs.


Collision data

Helping campaigners campaign

Getting more people cycling means improving the infrastructure on our streets so that everyone, whatever ability or level of confidence, is able to cycle easily and safely.

To achieve this, cycling campaign groups around the country work daily to make the case for cycling. They look at traffic consultations, propose changes to the highway, scrutinise planning applications, and work with local people and their local council to achieve these improvements.

Getting changes on the ground involves both a solid factual case for improvements as well as making an emotional case. For instance, reducing speed limits to tame traffic relies on having good access to collision data to demonstrate that there is a problem.

Thanks to our Outlandish Fellowship and some kind follow-on funding, we’ve been working on Bikedata, which is now ready as a beta site. (Beta means there are some problems we know about still but it’s good enough to start making public.)


Traffic counts

Data to make campaigning easier

The site gives you direct access to UK data for:

  • Collisions
  • Planning applications
  • Traffic counts
  • Cycle theft
  • Trip length (from CycleStreets journey planner)
  • Problems reported by cyclists
  • Photos (72,000+ images) for campaigning
  • Cycleability ratings of every street and path
  • Campaign groups around the UK



Cycleability ratings

In most cases, you can use filtering controls to show what you want to find. For instance, you can filter collision data to showing serious/fatal collisions at junctions. Or, perhaps you’d like to see all the reported places where cycle parking is needed:



Cycle parking problem locations – filtering in action

You can enable multiple layers at once. Our aim with this in the future will be to enable various correlations, e.g. showing how high pollution and traffic levels in an area might result in low levels of cycling.

You can also (again in most but not all cases), draw over an area to filter for that. Some layers also have an export facility enabled, so that you can easily obtain a spreadsheet of the same data as the map is showing.



Area drawing, to obtain an area for export

What layers do we want to add next?

  • Pollution
  • Taxi data (Cambridge only at this stage)
  • Census trip data
  • School travel data
  • … and more!

Next steps

We’re on the lookout for funding to enable us to develop this further. We’ve achieved everything you see with under £7k of funding, so think how much further the site could go.

Things we want to do include:

  • Change the UI so that it automatically ‘tells a story’
  • Add more data layers, e.g. pollution and accessibility analysis
  • Add charts to show change over time, ‘telling a story’
  • Add heat map views of several layers
  • Enable comparisons between Local Authority areas
  • Add a proper design and interface – the current UI is essentially a prototype
  • Enable more filtering controls
  • Ensuring all data is up-to-date, e.g. collision data needs an update
  • Add permalink URLs to enable all views to be persistent; currently this is only partially working
  • Fix oodles of bugs and inconsistencies

If you’d be interested in supporting any of the above developments, please do get in touch.

Also, code contributions are very welcome – the code is open source and on Github, and should be very easy to start working on, so let us know if you need advice, or just submit pull requests!



Lots of collisions, and the cycleability of the road is marked as low: 40%

Thanks

Thanks again to Outlandish Co-op, without whose funding and support would not have enabled us to get this project off the ground.

Lastly…

What should we call the site? Let us know your ideas!

CycleStreets.net in the Propensity to Cycle Tool

Wednesday, December 21st, 2016

A guest post from Robin Lovelace:

After 2 years in the making, the paper describing the Propensity to Cycle Tool (PCT), and the thinking behind it, has finally been published (Lovelace et al. 2016). The PCT is an online tool for helping to decide where to prioritise cycling policies such as new cycle paths.

The PCT would not have been possible without CycleStreets.net, so as well as describing the PCT and its use of their routing services, this article serves as a big Thank You from PCT to CycleStreets.net.

What is the Propensity to Cycle Tool?

For those new to the PCT, it’s an online tool for helping to decide where to prioritise cycling policies such as new cycle paths. It lives at www.pct.bike – check it out!

The context of its development is explained in the accompanying video on that page. This article reports how the tool itself works and how it uses data from CycleStreets.net.

The PCT is best understood by using it to explore current cycling levels, at regional, area, desire line, route and route network levels. We will take a look at how the PCT works at each of these levels, after a brief look at the scenario results at the regional level (the senarios are described in more detail in the paper).

Under the 2011 Census scenario, the PCT represents levels of cycling to work based on the Census. This is a reasonable proxy for levels of utility cycling overall. We used origin-destination (OD) data from the Census as the basis of the PCT as this is best publicly available dataset on English travel patterns. The input data is described in the paper and can be freely downloaded from the official UK Data Service website.

The regional picture and scenarios

The first thing the user sees on the front page is a map of England, broken into 44 regions:

We used deliberately large regions because successful cycling plans should be strategic and joined up, covering both large areas and large spans of time. This discourages the stop-start investment plans that have typified funding for active travel.

By hovering over different regions, the user can see what the current level of cycling to work is. We can discover that West Yorkshire has a low current level of cycling to work, 1.3% in the 2011 census, and that Cambridgeshire has a relatively high (but low by Dutch standards) level of cycling of 9.7%.

An exciting feature of the PCT is its ability to allow the user to imagine ‘cycling futures’. This can be seen on the front page map by clicking on the different scenarios (set to Census 2011 by default). We can see, for example, that under the Government Target to double cycling levels by 2025, West Yorkshire’s level would rise to 3.3% (more than a doubling) whereas Cambridgeshire would see cycling levels grow to 13.7% (a larger rise in absolute terms):

plot of chunk unnamed-chunk-2plot of chunk unnamed-chunk-2

Under the Go Dutch scenarios, these regions would see 23.1 and 13.5% of people cycling to work, respectively. This represents a huge leveling-out of cycling levels across the country, but still highlights the fact that some regions have higher cycling potentials than others, due to average trip distances and levels of hilliness.

Cycling levels at the area level

To launch the PCT for a region, click on it. Try clicking on West Yorkshire. You should be presented with the following image, which shows the area-based level of cycling to work from the 2011 Census. (When using the PCT, it is worth remembering that the visualisations work for every scenario.)

This shows that West Yorkshire has very low levels of cycling to work, hovering around 1% to 2% in most places. This suggests strongly that the region has low levels of utility cycling overall (despite the successes of the region’s sport cyclists). There is a cluster of zones with a higher level of cycling to the north of Leeds city centre (around Headingly) but even there the percentage of people cycling as their main mode of travel to work does not exceed 5%.

Cycling potential at the desire line level

This is all useful information, especially when we look at how the cycling potential could shift in the future. However, it provides little information about where current and future cyclists actually go. This is where the desire line level can be useful. This can be selected by clicking on the Straight Lines option from the Cycling Flows dropdown menu. The results (zoomed in for Leeds) are shown in the figures below (see Figure 3 in the paper).

What the above figures show is that as the level of cycling increases in a city, the spatial distribution of cycling can be expected to change. Under current conditions (be they related to socio-demographics or infrastructure or other factors), cycling in Leeds is dominated by the travel corridor to the north of the city centre. Yet there are clearly many short trips taking place from the south into the centre, as illustrated by the high cycling potential south of the city under the Go Dutch scenario.

Allocating cycling potential to the route network

This is where CycleStreets.net comes into play.

We know how many people go from A to B by cycling from Census data. But we have very little idea of how they are likely to travel. This is where the routing algorithm of CycleStreets.net comes in handy. We used the CycleStreets cycle routing API to estimate the ‘fastest’ route for all short (well, up to 20 km in Euclidean distance) desire lines in England.

Not only does CycleStreets.net allow us to find all the fastest routes, a good indication of where new infrastructure should be built as people (especially women and elderly) have a strong preference for cycling along the most direct routes.

The results of all this routing work is illustrated in the future below, which shows the fastest and quietest routes associated with the top cycled routes in Leeds.

Interestingly, the big fat line up to the north-west is Otley Road, well-known to have very high level of cycling. This also shows up in Strava data as having high current levels of cycling:

This is not formal validation but it is a good sign that the PCT and other data sources line-up for the current level of cycling. The big question is whether the PCT’s estimates of cycling levels under various cycling futures, including Go Dutch.

Here is not the place to answer such a question. Only the passage of time, and commitment from people (perhaps informed by models such as the PCT) to sustainable travel will help answer that one.

There is much more to say about the use of CycleStreets.net in the PCT but it gets rather technical very quickly.
Suffice to say at this stage that it involved writing lots of code in R, a language for statistical programming, and that this has now resulted in the publication of stplanr, an R package for sustainable transport.

(For more on how to install R and (for bells and whistles) RStudio, which this blog post was written in, please see the relevant sections of the book Efficent R Programming (Gillespie and Lovelace, 2016).)

With R installed, stplanr can be installed with:

install.packages("stplanr")

With this package installed, you can start using the CycleStreets.net routing algorithm with the following function:

library(stplanr)
route = route_cyclestreet(from = "Leeds", to = "Cambridge")

which results in spatial data, which can be visualised as follows:

library(leaflet)
leaflet() %>% addTiles() %>% addPolylines(data = route)

There is much more I could say about the technical side of things but at the request of the editors I will leave it there for now. For more info please see the stplanr vignette.

I plan to follow this overview article up with a more technical blog post in the New Year. Watch this space!

Reference

Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., & Woodcock, J. (2016). The Propensity to Cycle Tool: An open source online system for sustainable transport planning. Journal Of Transport And Land Use, 0. doi:10.5198/jtlu.2016.862

Making data more usable by campaigners: our Outlandish Fellowship

Wednesday, August 24th, 2016

We’re really pleased to announce that we’ve been selected for the Outlandish Fellowship to help local cycle campaigners by expanding our collisions data pages into a broader resource covering more types of data (e.g. traffic counts, pollution) and add lots of new ways to access it.

Helping campaigners campaign

Getting more people cycling brings a more sustainable and efficient transport system, improved public health, and greater access to employment. However, in the UK, cities have failed to make space for cycling on our streets, preventing mass uptake.

We know from our own activity as campaigners in Cambridge, that making a good evidence­-base for reallocating roadspace or challenging poor developments involves significant work. For instance, developers often claim that their route has “good connections to the local cycle network” whereas in practice we know that this often means a shared-use path that is hard to access.

We lack the data to make a strong case that, for instance, a combination of a high collision rate, congestion, pollution in an area means that a developer or a Local Authority needs to improve their plans. Of course there remains the need for making arguments based on broader policy, such as that cycling should be prioritised as a positive and healthy form of transport, but hard data for specific cases helps backs this up.

What kind of data is out there?

There’s lots out there that could be useful for cycle campaigning. Things like collision data (which we’ve already done a bit of work on), traffic count data, travel time data, census travel data, on-street counts, etc. Imagine if, instead of having to search these out and find someone technical to process it, you could simply point and click, with national coverage?

Collisions

This data is becoming available but it’s very scattered, meaning that correlations are hard to make. It’s often in raw formats that need significant work before it can be understood, or hidden in Local Authority websites that are not sufficiently flexible or easy for non-specialists to use. Often it’s not arranged for the kinds of tasks that cycle campaigners specifically need.

We’re aiming over time to build up a multi­functional resource to help build this case, enabling users to a build and link to an interactive display of the relevant data (involving multiple layers, clickable points, reports, summary info) for a particular location or route, that they can use in their advocacy and liaison work.

Mark, better known as ‘Ranty Highwayman’ in cycle planning circles, said:

“The project looks really exciting. From my point of view, the ability to generate information from one place is a great idea as at the moment, it’s a really labour-intensive process, this could create maps for reports, committee papers etc.”

Our plan is that it would be available for embedding in local campaign websites, exporting to reports, used in apps, and so on.

Some examples

Here are just a few sample stories that we’ve come across in our own work as cycle campaigners, some from Cambridge:

  • Justification of removal of one-way street restrictions for cycling. In previous decades, traffic planning favoured one-way streets as a way to regularise traffic flows and avoid rat-running. However, the side-effect is to stop easy cycling. If we could compare collision data easily in a particular location, we could show how streets that have been made two-way for cycling haven’t caused a safety hazard.
  • Worsened likelihood of collisions in areas with an existing poor record. A supermarket developer wanted to open a local store under a just-in-time delivery regime in a high street with a narrow carriageway that has heavy traffic and high pedestrian and cycle flows. A good evidence base, combining flow level data, Origin/Destination data, collisions and traffic data delay data, would have enabled us to argue that the developer will need to amend their delivery plans to be more sympathetic to the local circumstances.
  • Higher levels of pollution in areas with significant problems already. Areas with many schools particularly need to avoid pollution. A developer proposes a new estate in such an area but fails to provide good connections into the site for walking and cycling. A better evidence base, combining socio-economic data, school travel data, pollution and cycling levels would help us convince the Local Authority that the developer needs to provide this connectivity.

What changes can people expect?

We’ve started from our collision data viewer as the base, and to this we’re adding:

  • Completely reworking the search facility so that it’s actually useful – currently it’s stuck in a prototyped state, with lots of non-useful fields. This will mean that common scenarios like “Collisions between a date range in area X” are possible.
  • Adding typical scenarios as new front-end ways to access it. Currently, it’s very map-based, whereas we want to enable common use-cases much more easily.
  • Making everything Local Authority -aware. Currently it’s all manual boundaries, but we’d like users to be able to do things like compare casualty rates (and other data – see below) between areas.
  • Upgrading the interface. We’ve now got some nice new icons for a start :)
  • Adding a better way to import the data. Currently, updating it each year is not as easy as we’d like, and new data types (see below) need to be supported.
  • Adding generalised origin-destination data for areas, using analysis from our own journey planner
  • Adding traffic count data, from the DfT
  • More data (in future – after the current Fellowship work)
  • Adding the ability to switch between multiple layers of data
  • Making all the above available through a more generalised Advocacy data API. In fact, this will be the system powering all the above!
  • Adding the ability to embed custom views of the data in other sites

The code will be open source too :)

We’ll be giving updates via this blog over the coming 6 weeks – stay tuned!

Collisions

Blackfriars Bridge, scene of many unfortunate collisions over many years. With the new data platform, it will be possible to make easy comparisons about how the introduction of the new Dutch-standard cycle infrastructure just built reduces these collisions.

Outlandish

OutlandishOutlandish is a web agency based in Finsbury Park, down the train from us in Cambridge. The members of Outlandish want to unleash technology’s potential to make the world a fairer, better place. It’s a worker co-operative and invests all surpluses into projects that help achieve the members’ goals. They build digital applications and websites for companies, charities and universities that make their lives easier and help them to discover and communicate new insights from their data.

Outlandish has made available fellowships for people who are using the Internet and digital technologies to address social issues. The fellowships include funding and other forms of support to allow participants to start their own projects. The aim of the fellowship is to support work that matches the mission of Outlandish, and to expand the network of people that they actively collaborate with.

We’re really proud to be in the first set of Fellows, and it’s going to be great to be working with a co-op!

  
Photos from the launch of the Outlandish Fellowhip

Our project team

Our main developer on this project is Martin, doing most of the work, as the Outlandish Fellow.

He’s being helped by Simon (CycleStreets’ other principal developer), when he can be wrestled away from interesting routing quality challenges like turn delays that we’ve been working on recently.

We’ve also set up a Stakeholder board, to ensure that the data work we’re doing is genuinely useful. This is:

A version of this blog post also appears on the Outlandish blog.

CycleHack Cambridge 2016

Sunday, April 10th, 2016

CycleHack is a 48-hour event aiming to make cities cycle-friendly through reducing the barriers to cycling and prototyping new ideas to improve the cycling experience and encourage more and safer cycling. More than 40 other cities around the world have signed up to host CycleHack events in their communities over the weekend of 24 to 25 June, 2016.

Cyclehack

Cambridge, home of CycleStreets, will be joining cities around the world for a weekend-long CycleHack event, which will be held at Anglia Ruskin University and other locations where specialist equipment may be required. Participants will be encouraged to test their ideas and prototypes around town during the event.

CycleHack was launched in 2014 in Glasgow and has since grown to a global event. In 2015 CycleHack had more than 600 participants from over 25 countries across five continents. 67% of participants were inspired to cycle more. In 2016 the event is set to be even bigger with more than 40 cities already registered.

Cyclehack CambridgeCycleHack Cambridge is hoping to attract a whole range of people from developers, makers and data scientists to non-technical artists, designers and those who are interested in cycling and have some great ideas. We also want to include representation from all corners of our diverse cycling community and want to see lots of students and young people taking part. This event will bring together the key elements of our Cambridge culture: cycling, innovation and technology.

The event encourages participants to prototype and test their ideas during the weekend to see how they will work in their intended context. Solutions can fall into one of the five CycleHack categories; digital, physical, policy, local plan, event/campaign. Hacks will be loaded to the online open source catalogue to show how the ideas can be replicated. Prizes will be awarded to the best hacks.

CycleStreets is one of the partners organising the event. We’ll be on hand to help out and give advice to people considering doing a digital hack. Perhaps you’ve never used an API (a data interface – like the CycleStreets API for instance) before or don’t know what it is? We can help you get started.

Cambridge Cycling Campaign is the main organiser of the event. Other partners include the Smart Cambridge Programme (Cambridge County Council) and CoDE Research Institute at Anglia Ruskin University.

There are more details on the Facebook event page about the event, and you can register online.

We welcome your feedback, especially to report bugs or give us route feedback.

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