In the middle of the 9th Century, the territories of mainland Britain were in constant flux, with power shifting between the established Anglo-Saxon kingdoms and Viking settlers.
Towards the end of the century, the battles and power shifts reached a kind of equilibrium, with Alfred King of Wessex and Guthrum the Danish warlord agreeing a treaty defining the boundaries of their kingdoms.
One of these boundaries was demarcated by Watling Street, an ancient trackway that stretched from Shrewsbury in the west of England to the Thames estuary in the east.
The boundaries of Britain are different today, but the vestiges of this ancient divide remain in the names of the places that surround us.
To illustrate this, mySociety has been working with the British Museum, with data sourced from the University of Nottingham’s Institute for Name-Studies. We’ve created a simple interactive map as part of their Vikings Live event to show the Norse influence on around 2,000 place names in different parts of the United Kingdom and Ireland.
For each place, we’re showing its etymology, a breakdown of the different elements that make up its name and a link to the nearest cinema that will be showing the British Museum’s Vikings Live—a private view of the BP exhibition Vikings: life and legend in the company of world experts, presented live in your local cinema.
So, for a completely different perspective of the place names near your home, head over to the British Museum’s site to explore the influence the Vikings had on the names where you live. And, next time you’re in a Thorpe, a Howe, a Kirkby, or even in Grunty Fen (our favourite place name), think of the Vikings who’ve left an indelible mark on the toponymy of the United Kingdom and Ireland.
A commission from the Welsh Government has resulted in new functionality for Mapumental, which now has the capability to display multiple points and to produce RAW data compatible with GIS applications. Here’s how it happened.
How accessible is your nearest school, post office, or GP’s surgery? In Wales, that’s not always a simple question: the country’s mountainous landscapes, rural populations, and sometimes infrequent bus services can mean that those without cars are rather cut off from public service provision.
But of course, like any other authority, the Welsh Government has an obligation to quantify just how accessible their services are.
For many years, they have done so using a number of different methods. Some of these involve literally millions of point to point calculations – so, naturally, when Bill Oates, Head of Geography & Technology, Knowledge Services at the Welsh Government, approached Mapumental, he was keen to discover whether we could simplify things.
We were keen to try it, too – plotting multiple points would add a whole new slew of possibilities to Mapumental. Previously, Mapumental has been all about travel from a single point, and this functionality would bring new applications across all kinds of industries and users.
There’s only one way to find out
The sensible way forward was to pick a single service and see what we could do. One of the government’s open data sets showed positioning of all the secondary schools in the country, and would give us a very good indication of how manageable the task would be across all other provisions.
So we set ourselves this aim: to display the shortest transit time to get to any secondary school in Wales, from any point in that country.
This project was not like the map we made for the Fire Protection Association last year, with its postcode input and interactive sliders. It bore more relation to our static maps, but with the additional dimension that the single map would have multiple points plotted on it. Each point would display its own associated journey times, and where travel to one school was quicker than to another, it would have to override the data of the school that was further away.
And here’s the (very pretty) result
Transit times by public transport to secondary schools in Wales, with an arrival time of 9:00am.
Time bands are in 15-minute increments, with red areas being those where schools are accessible within a 15-minute journey (the centres of the red dots therefore also represent the positions of the schools).
Purple areas are those where journey time is between 1.75 and 2 hours, and the colours in between run in the order you see bottom right of the map. White areas (much of which are mountainous and sparsely-populated) are outside the two-hour transit time.
But there’s more – data for GIS
Plotting all the schools on a single map required quite a bit of modification to Mapumental, but there was another important part of the project that also had to be worked on, if the output was to meet all the needs of the Welsh Government.
They needed to be able to export the raw transit time data to their own GIS tools – the tools that they use to feed into official statistics. This allows the transit time data to be combined with other datasets, such as population density, for in-depth analysis.
We added a feature which allows Mapumental to produce what is known as a ‘raster grid’ output – basically, an enormous matrix that gives every pixel on the map a travel time value. To do this, we used the open source GRASS format.
Bill Oates is keen to see where this project can go:
“I’m really excited at the prospect of combining the power of Mapumental with our open data, and fully understanding how accessible Welsh public services are by public transport.”
To him, the benefits are clear:
“Mapumental’s approach is significantly quicker than our current methods, so this work will help save us time as well as providing a more engaging output.
“We hope that future work with mySociety will give us a sustainable approach to calculating the accessibility of local shops, hospitals, post offices and other services on an ongoing basis to help ensure that we’re meeting the needs of our citizens.”
We’re looking to build on our success, and offer this service to others – initially on request but via our API as soon as we can. We’ll keep you posted as to our progress.
You can see multiple-point mapping in action, on our Mapumental Property project – now the tool allows house-hunters to take more than one person’s commute into consideration when choosing where to live.
Who might use Mapumental?
Now that Mapumental can plot transit times from multiple points, and provide RAW data for GIS applications, we have great potential for use by anyone interested in travel and accessibility. That could be in central and local government strategy, town planning, architectural consultancy, transport provision, large enterprises looking to save on parking, or start-ups in the green transport space…to name but a few.
Could Mapumental help you with your mapping needs? If so, please do drop us a line at email@example.com.
Photo of Welsh school bus (bws ysgol) by Aqwis (CC)
If you’re searching for a new home, give Mapumental Property a try. lt narrows property results down, only showing you houses that fall within a decent commute time from the places you visit regularly – like work, school, or the shops. Here, have a go – it’s fun.
Irritation is the mother of invention
Several years ago, some of our colleagues were looking for a house to rent.
They weren’t set on a particular town. There were two important factors: that it was within a reasonable commute from central London, where they frequently attended meetings; and that the rent was affordable.
Faced with these requirements, most of us would sift through property sites and cross-reference the listings manually with public transport information. It’s rather time-consuming, and slightly irritating, but hey-ho, it has to be done.
But mySociety is in the business of building useful web tools, so when something irritates us like this, we look to see if we can solve the problem through the magic of code. In a stroke of good timing, it was at just around this time that the Department for Transport approached us to ask us to work with their public transport data – and Mapumental was conceived.
The key was to combine Ordnance Survey postcodes with the DfT’s data about journey times, NPTDR (National Public Transport Data Repository). This data set takes a ‘snapshot’ of every public transport journey in Great Britain for a selected week in October each year.
Sounds simple? The process was not without its challenges. Prime among them was the problem of displaying map tiles, plus the vast quantities of transport data, within a reasonable amount of time, no matter which postcode or zoom level the user chose. As we know, a ‘reasonable amount of time’ for a page to load is a metric which is forever shrinking.
By 2006, we had created Mapumental’s first iteration. Users could input a postcode and see all areas of the country that could be reached by public transport, divided into coloured travel-time bands. In 2009, Francis Irving, the mySociety coder behind Mapumental’s early endeavours, explained the technology he’d used. It was Flash-dependent, and a few years later, developer Duncan wrote about some of the technical hurdles he overcame replacing the Flash elements, in view of the rise of the iPhone, which famously doesn’t ‘do’ Flash.
Hoorah! Now our colleagues could type in a central London postcode and see everywhere that fell within a 40-minute journey from there. It wasn’t long before we added median house price data, too.
Beauty is in the eye of the crowd
We even added a ‘scenicness’ rating: if the beauty of your surroundings was important to you, you could rule out anywhere below a certain level of attractiveness.
How did we assess how scenic every area in the UK is? By crowdsourcing the information – our ScenicOrNot website displays a random photograph from every square mile of the British isles, inviting people to rate them. It is surprisingly compulsive.
A showcase tool
Mapumental may have been born from our own needs, but we knew from the beginning that it would have wider applications. It has always been the sort of project that got people excited, once they saw it in action.
We wanted to show how elegantly Mapumental can handle all kinds of data, starting with houses for sale and rent – so we developed Mapumental Property. It’s not intended as a serious competitor to the giant property websites out there. Rather, it’s an all-singing, all-dancing demonstration of Mapumental’s strengths.
In this case, the data is from the property website Zoopla, and you can narrow it down to show rental or sales property within your chosen price bands and commute distances. You can even add multiple destination points, so that households of two or more people can find their optimum location.
But Mapumental is not just about property: swap out that Zoopla layer, and you could put in anything else you can imagine – hospital locations, supermarkets, schools, job vacancies… you name it.
The beauty of Mapumental is that now we’ve done the really hard part, incorporating new data layers is relatively simple. Recent work for the Fire Protection Association and the Welsh Government, among others, has shown its versatility.
Now how about you?
We believe that Mapumental’s possibilities are pretty much endless. Have you got an unloved, difficult-to-navigate dataset that Mapumental could breathe new life into? Or would your stakeholders benefit from being able to see your data displayed on a map? Let us know.
All of us at mySociety love the fact that there are so many interesting new civic and democratic websites and apps springing up across the whole world. And we’re really keen to do what we can to help lower the barriers for people trying to build successful sites, to help citizens everywhere.
Today mySociety is unveiling MapIt Global, a new Component designed to eliminate one common, time-consuming task that civic software hackers everwhere have to struggle with: the task of identifying which political or administrative areas cover which parts of the planet.
As a general user this sort of thing might seem a bit obscure, but you’ve probably indirectly used such a service many times. So, for example, if you use our WriteToThem.com to write to a politician, you type in your postcode and the site will tell you who your politicians are. But this website can only do this because it knows that your postcode is located inside a particular council, or constituency or region.
Today, with the launch of MapIt Global , we are opening up a boundaries lookup service that works across the whole world. So now you can lookup a random point in Russia or Haiti or South Africa and find out about the administrative boundaries that surround it. And you can browse and inspect the shapes of administrative areas large and small, and perform sophisticated lookups like “Which areas does this one border with?”. And all this data is available both through an easy to use API, and a nice user interface.
We hope that MapIt Global will be used by coders and citizens worldwide to help them in ways we can’t even imagine yet. Our own immediate use case is to use it to make installations of the FixMyStreet Platform much easier.
We’re able to offer this service only because of the fantastic data made available by the amazing OpenStreetMap volunteer community, who are constantly labouring to make an ever-improving map of the whole world. You guys are amazing, and I hope that you find MapIt Global to be useful to your own projects.
The developers who made it possible were Mark Longair, Matthew Somerville and designer Jedidiah Broadbent. And, of course, we’re also only able to do this because the Omidyar Network is supporting our efforts to help people around the world.
From Britain to the World
For the last few years we’ve been running a British version of the MapIt service to allow people running other websites and apps to work out what council or constituency covers a particular point – it’s been very well used. We’ve given this a lick of paint and it is being relaunched today, too.
MapIt Global is also the first of The Components, a series of interoperable data stores that mySociety will be building with friends across the globe. Ultimately our goal is to radically reduce the effort required to launch democracy, transparency and government-facing sites and apps everywhere.
If you’d like to install and run the open source software that powers MapIt on your own servers, that’s cool too – you can find it on Github.
About the Data
The data that we are using is from the OpenStreetMap project, and has been collected by thousands of different people. It is licensed for free use under their open license. Coverage varies substantially, but for a great many countries the coverage is fantastic.
The brilliant thing about using OpenStreetMap data is that if you find that the boundary you need isn’t included, you can upload or draw it direct into Open Street Map, and it will subsequently be pulled into MapIt Global. We are planning to update our database about four times a year, but if you need boundaries adding faster, please talk to us.
If you’re interested in the technical aspects of how we built MapIt Global, see this blog post from Mark Longair.
Commercial Licenses and Local Copies
MapIt Global and UK are both based on open source software, which is available for free download. However, we charge a license fee for commercial usage of the API, and can also set up custom installs on virtual servers that you can own. Please drop us a line for any questions relating to commercial use.
It’s high time we updated you on Mapumental, our journey-time mapping project. For those who may not remember, Mapumental is based on a simple idea: to visualise transit times, by public transport, from or to any postcode in Great Britain.
It all began in 2006, when the Department for Transport approached us to see what we might do with public transport data; in 2009 we won an investment loan from Channel 4 and Screen West Midlands which enabled us to build a beta tool – you might have played with it. If not, go on, have a go. It’s fun!
It’s been quite a long journey to where we are today. Unlike many mySociety projects, funding for Mapumental’s development came from a commercial investment loan, with a condition that we set it up as a business. For that reason, it’s not enough that it’s beautiful and useful – we need to find ways for it to be profitable, too. All revenues are set to come back to fund our not-for-profit activities.
We could tell from very early on in the project that Mapumental would be a sought-after tool for all sorts of purposes, from business to personal use. For example, you can see commute times at a glance, so it’s great for house-hunters and job-seekers. Consequently, it’s also great for the property and recruitment industries.
“Your maps look amazing, such a great way of representing what could be really boring data, but isn’t.” – A jobseeker
We can see loads of other possibilities too – like urban planning. This sort of analysis would have been far more expensive in the past; with Mapumental, planners can see at a glance how accessible a new development would be by public transport. Its potential uses are wide-ranging, answering questions for businesses, organisations, charities, and public facilities – especially those wanting to maximise accessibility or encourage use of greener transport options.
“The maps are a fantastic, a great tool and should be used for every planning application. I will be using Mapumental for all of our projects!” – Lee Taylor, Veridis Design
We’ve recently refined a product that’s pared down from the dynamic maps you may remember from that beta tool: static maps. These are simple, non-interactive maps which show transit time in bands. They’re flexible in that they can be generated for any postcode, with any maximum travel time, and depict travel at any given time of day.
We can provide a one-off map for personal use, or batches of many thousands of maps – as we have done for estate agents Foxtons, who now have a Mapumental map on every property listing.
As we generate more and more maps for different uses, showing different parts of the country, we’re really enjoying digging out all sorts of surprising facts – like how it’s quicker to travel from Watford to Westminster than it is from some parts of Harringay. Or how Cardiff University students might sensibly live at all points east as far as Newport, but will be stymied for transport in the west if they live anywhere other than Barry or Bridgend.
In fact, our very favourite use so far has come from an individual who centred his map around his home postcode. He tells us he has printed it off and put it up by the front door, so that on his way out of the house, he can find a new and surprising destination for day-trips.
Find out more on the Mapumental website – and please do spread the word among friends and colleagues who might benefit from a Mapumental map.
We’ve added a variety of new features to our postcode and point administrative area database, MaPit, in the past month – new data (Super Output Areas and Crown dependency postcodes), new functionality (more geographic functions, council shortcuts, and JSONP callback), and most interestingly for most people, a way of browsing all the data on the site.
- Firstly, we have some new geographic functions to join touches – overlaps, covered, covers, and coverlaps. These do as you would expect, enabling you to see the areas that overlap, cover, or are covered by a particular area, optionally restricted to particular types of area. ‘coverlaps’ returns the areas either overlapped or covered by a chosen area – this might be useful for questions such as “Tell me all the Parliamentary constituencies fully or partly within the boundary of Manchester City Council” (three of those are entirely covered by the council, and two overlap another council, Salford or Trafford).
- As you can see from that link, nearly everything on MaPit now has an HTML representation – just stick “.html” on the end of a JSON URI to see it. This makes it very easy to explore the data contained within MaPit, linking areas together and letting you view any area on Google Maps (e.g. Rutland Council on a map). It also means every postcode has a page.
- From a discussion on our mailing list started by Paul Waring, we discovered that the NSPD – already used by us for Northern Ireland postcodes – also contains Crown dependency postcodes (the Channel Islands and the Isle of Man) – no location information is included, but it does mean that given something that looks like a Crown dependency postcode, we can now at least tell you if it’s a valid postcode or not for those areas.
- Next, we now have all Lower and Middle Super Output Areas in the system; thanks go to our volunteer Anna for getting the CD and writing the import script. These are provided by ONS for small area statistics after the 2001 census, and it’s great that you can now trivially look up the SOA for a postcode, or see what SOAs are within a particular ward. Two areas are in MaPit for each LSOA and MSOA – one has a less accurate boundary than the other for quicker plotting, and we thought we might as well just load it all in. The licences on the CD (Conditions of supply of SOA boundaries and Ordnance Survey Output Area Licence) talk about a click-use licence, and a not very sraightforward OS licence covering only those SOAs that might share part of a boundary with Boundary-Line (whichever ones those are), but ONS now use the Open Government Licence, Boundary-Line is included in OS OpenData, various councils have published their SOAs as open data (e.g. Warwickshire), and these areas should be publicly available under the same licences.
- As the UK has a variety of different types of council, depending on where exactly you are, the postcode lookup now includes a shortcuts dictionary in its result, with two keys, “council” and “ward”. In one-tier areas, the values will simply by the IDs of that postcode’s council and ward (whether it’s a Metropolitan district, Unitary authority, London borough, or whatever); in two-tier areas, the values will again be dictionaries with keys “district” and “council”, pointing at the respective IDs. This should hopefully make lookups of councils easier.
Phew! I hope you find this a useful resource for getting at administrative geographic data; please do let us know of any uses you make of the site.
I’m very pleased to announce that mySociety’s upgraded point and postcode lookup service, MaPit, is public and available to all. It can tell you about administrative areas, such as councils, Welsh Assembly constituencies, or civil parishes, by various different lookups including name, point, or postcode. It has a number of features not available elsewhere as far as I know, including:
- Full Northern Ireland coverage – we found a free and open dataset from the Office of National Statistics, called NSPD Open, available for a £200 data supply charge. We’ve paid that and uploaded it to our data mirror under the terms of the licence, so you don’t have to pay – if you feel like contributing to the charity that runs mySociety to cover our costs in this, please donate!
- Actual boundaries – for any specific area, you can get the co-ordinates of the boundary in either KML, JSON, or WKT – be warned, some can be rather big!
- Point lookup – given a point, in any geometry PostGIS knows about, it can tell you about all the areas containing that point, from parish and ward up to European electoral region.
- History – large scale boundary changes will be stored as new areas; as of now, this means the site contains the Westminster constituency boundaries from both before and after the 2010 general election, queryable just like current areas.
If you wish to use our service commercially or are considering high-volume usage, please get in touch to discuss options; the data and source code are available under their respective licences from the site. I hope this service may prove useful – we will slowly be migrating our own sites to use this service (FixMyStreet has already been done and already seems a bit nippier), so it should hopefully be quite reliable.
Thanks must go to the bodies releasing this open data that we can build upon and provide these useful services, and everyone involved in working towards the release of the data. Thanks also to everyone behind GeoDjango and PostGIS, making working with polygons and shapefiles a much nicer experience than it was back in 2004.
It’s very simple to do:
- Go to FixMyStreet, and locate any RSS feed of the latest reports you want (for the above map, I used Edinburgh Waverley’s postcode of EH1 1BB; you could have used reports to a particular council, or ward, using the Local alerts section). Copy the URL of the RSS feed.
- Go to Google Maps, paste the RSS feed URL into its search box, and click Search Maps.
- Click the “Link” link to the top right of the map, and copy the “Paste HTML to embed in website” code.
- Paste that code into your blog post, sidebar, or wherever (you can alter the code to change its size etc.).
The latest reports from FixMyStreet, superimposed on a Google Map, embedded in your blog. Hope that’s helpful.
Here is a diagram of how the backend of Mapumental works. Take it in the spirit that Chris Lightfoot set when he made a similar diagram for the No. 10 petitions site – although many such diagrams are useless, hopefully this one contains useful information.
(Click on the diagram for a large version)
Below, I’ve explained what the main components are, and some interesting things about them.
Everything can, at least in theory, run on lots of servers. Currently we are only actually using one server for web requests, because of problems with HAProxy. We’re runnning isodaemons on two different servers.
Basic web application – it started out as raw Python, but the more Matthew hacks on it the more Django libraries he pulls in. Soon it’ll be indistinguishable from a Django app. When someone enters a new postcode, it adds it to the work queue in the PostgreSQL database, then refreshes waiting for the job to be finished. Then it displays the flash application (made by Stamen), set up to load the appropriate tile layers.
Tile server and cache – This uses the Python-based TileCache, calling Geospatial Data Abstraction Library (GDAL) to help render the tiles from points. It was originally written by Stamen, and expanded by mySociety. GDAL isn’t perfect, it doesn’t have fancy enough algorithms for my liking. e.g. Using a median rather than a weighted mean.
Isodaemons – These are controlled by a Python script, but the bulk of the code is custom written in C++. Slightly crazily, this can find the quickest route by public transport for each of 300,000 journeys from every station in the UK to a particular station, arriving at a particular time, in 10 to 30 seconds.
I had no idea how to do this, but luckily I live in Cambridge, UK. It’s a city fit to bursting with computer scientists. Many of the jobs are dull, and need little computing, never mind science – like writing interface layers for SQL server. So if you have a real interesting problem it’s easy to get help!
The universal advice was to use Dijkstra’s algorithm, which needed a bit of adaptation to work efficiently over space-time, rather than just space. Normally it is used for planning routes round a map, but public transport isn’t like that, you have to arrive in time for each particular train, so time affects what journeys you can take.
I originally wrote it in Python, which was not only too slow, but used up far far too much RAM. It could never have loaded the whole dataset in. However, the old Python code is still run by the test script, to double check the C++ code against. It is also still used to make the binary timetable files, see below.
Travel times, 1 binary file / postcode – I briefly attempted to insert 300,000 rows into PostgreSQL for each postcode looked up, but it was obvious it wasn’t going to scale. Going back to basics, it now just saves the time taken to travel to each station in a simple binary file – two bytes for each station, 600k in total. The tile server then does random access lookups into that file, as it renders each tile. It only needs to look up the values for the stations it knows are on/near the tile.
There’s various other bits:
- cron jobs for sending out invites
- converting timetable data from ATCO-CIF to the binary format
- loading static layer data into the database
- precaching every tile for static datasets
- Squid and Apache and FastCGI both sit in front of the web applications
- for speed, we cache the mapping background tiles from Cloudmade
- when zoomed out, there is code to cull which stations are used to draw tiles
- of course, a bunch of test code
Thanks to everyone who helped make Mapumental, we couldn’t have done it without lots of clever people.
I realise the above is a sketchy overview, so please ask questions in the comments, and I’ll do my best to answer them.
mySociety would never have been able to make Mapumental in the way we did if it wasn’t for the help of San Franciso-based geovisualisation gurus Stamen. They came up with the brilliant idea of sliders instead of static contours lines, they built the flash front end, and, crucially, they helped make sure all the contours had just the right degree of splodginess for a satisfyingly splodgy user experience.