GISRUK 2014 (Part 1)

I was at the Geographic Information Systems Research United Kingdom (GISRUK) 2014 conference last week. GISRUK is the key GIS conference for early-career academic researchers in the UK and Ireland, and is hosted by a different university in the British Isles each year. The audience are mainly UK academics, with young researchers and professors in roughly equal attendance, along with some academics from abroad, including Malaysia, Nigeria and Canada. They are definitely more geo and less tech, the conference being relatively quiet on Twitter, especially compared to conferences such as State of the Map or Wherecamp EU.

This year the conference was hosted up at Glasgow University. Being tucked into the Easter break might have meant a reduced attendance on previous years. However, there were many good talks in the two parallel streams that ran through the three days of the conference – some 50 talks altogether, plus plenaries – and some talks were very popular, with attendees just about squeezing in to the venue.

In this post (and in the second and third parts, to follow) I’ve highlighted the talks that I found the most interesting. Of course, with two streams, there were inevitably interesting sessions which overlapped, and so I may have missed some of the best of all – in a couple of cases I ended up changing room half-way though a session. I’ve paraphrased the talk titles here.

Streets vs landmarks for text-based directions for pedestrians

This talk, given by William Mackaness from Edinburgh, was on an interesting study monitoring how people get from A to B, given one of two kinds of text directions – landmark based “turn left at the Bank of Scotland branch coming up on the left” or street based “continue on George Street, turn left onto Frederick Street in 500m” and monitored, with GPS and movement sensors, how well they moved through the urban realm, with landmark based directions proving better. Of course, these are harder for automated systems as street names a more uniform and consistent storage type than landmarks.

Clustering landmark tags in urban images

This was probably my favourite talk of the whole conference. By the same team as the above, it was presented by Phil Bartie (St Andrews) and outlined algorithms used to detect buildings and other landmarks from photos, by looking at where people tag interesting features in set photographs, how they tag them, and then linking the tags and locations together to try and separate visually close (but distinct) features, and combine different elements of the same feature that are spatially far apart. The heatmap examples used in the talk were compelling.


Using social media data to assess crime hotspots

Nick Malleson’s (Leeds) talk looked at tackling the “daytime population” problem – crime statistics tend to exaggerate city centres, as these have a large daytime population but a low residential (i.e. census/official) population, which areas are typically normalised by to produce a crime rate. By looking at georeferenced social media activity as a proxy for daytime population, the city centre hotspots disappear and move into the most deprived suburbs – although these need to be controlled also by a possible lower-than-average use of social media in such areas.


Exploring links between coal-mining, deprivation and health

This known link was mapped out well by Paul Norman (Leeds), using some great maps of the relevant census data. The talk included a potted history of coal mines and their phased closures. The study was longitudinal – combining statistics over multiple censuses, with data on opening and closing of mines (mine opening dates often being hard to determine).


At the end of the first day of the conference, therewas a reception at the opulent City Chambers in the centre of Glasgow, where I had the novelty of being served a glass of Irn Bru (Scotland’s other national drink, and tougher to find in London) by a waiter, in a room surrounded with marble and various paintings of former council leaders!


Part two to follow tomorrow. Addy Pope at EDINA Go-Geo has also reviewed the conference.

Visit the new Shop
High quality lithographic prints of London data, designed by Oliver O'Brien
Electric Tube
London North/South

5.5 Million Journeys at NYC Bike Share


[Updated - timeperiod-split maps added] Following on from my London bikeshare journeys graphic, here is the same technique applied with the data released by NYC Bike Share (aka Citi Bike) earlier this week.

If you look carefully at the full size map you can see a thin line heading north-eastwards, initially well out of the bikeshare “zone”, representing journeys between Williamsburg and Central Park, via the Queensboro Bridge cycle path. We see a similar phenomenon for journeys between Tower Bridge and Island Gardens in London. Whether any of the riders actually take this route, of course, is open to question – they might take a longer – but more familiar – route, that stays more within the area of the bikeshare.

Below is a version of the graphic with the data split into four timeperiods – weekday rush-hour peaks (7-10am and 4-7pm starts), weekday interpeak (10am-4pm), weekday nights (7pm-7am) and finally weekends. The data is scaled so that the same thicknesses of lines across the four maps represent the same number of journeys along each street segment – but bear in mind that there are fewer weekends than weekdays. While, as would be expected, the rush-hour peaks see the most number of journeys, there is less spatial variation across the city, between the four timeperiods, than I expected. Click on the graphic for a larger version.


The graphics were produced by creating idealised routes (near-shortest path, but weighted towards dedicated cycle routes and quieter roads) between every pair of the ~330 docking stations in the system, using Routino and OpenStreetMap data (extracted using the Overpass API). Edge weights were then built up using a Python script, a WKT file was created and then mapped in QGIS, with data-based stroke widths applied from the weights.

The routes are only as good as the OpenStreetMap data – I think the underlying data is pretty good for NYC, thanks to great community work on the ground, but there is still a possibility that it has missed obvious routes, or proposed wacky ones. It also doesn’t account for journeys starting or ending at the same place, or journeys where the prime purpose is an exploration by bike – with the user unlikely therefore to take an “obvious” A-B route.

Even with that caveat, it’s still a revealing glimpse into the major route “vectors” of bikeshare in New York City.

Visit the new Shop
High quality lithographic prints of London data, designed by Oliver O'Brien
Electric Tube
London North/South

A Census for Open Data in Cities


The Open Knowledge Foundation (OKFN) have produced a census for government open data availability for countries around the world, known as the Open Data Index. Each country is assigned scores for 10 attributes on openness and accessibility for each of 10 types of data (such as election results and pollution information). Currently the United Kingdom is at the top of the table.

More recently, OKFN expanded the concept to look at open data for cities within each country, in other words data that is managed at the City Hall level. For example, there is a project page for individual cities within the UK. This time, 15 types of data are examined, again each gaining up to 10 points for openness. The project is still in its information gathering stage so, at the time of writing, only 6 cities have their data partially, or fully, entered. The census for Italian cities, for example, is looking more complete.

Such a census is of great interest when building an application like CityDashboard, which is currently available for eight cities around the UK. Although CityDashboard doesn’t only use open data sources, those which do have documented APIs, open data licences and machine readable formats greatly aid building and expanding a website such as CityDashboard. CityDashboard takes in social media and sensor data, as well as “official” data of the sort that is being categorised by the OKFN project, but some data, such as live running information for metro services, will quite likely always best come from the official sources.

As such, I will keep a close eye on this project. Cambridge and Sheffield look like two promising cities for which the necessary official data is both available and open, which would make implementing them in CityDashboard relatively straightforward.

The census is user-driven and reviewed, so it’s up to you to get information on the availability (or lack) of data for your local city catalogued in the census.

A Changing City – OS Open Data Reveals a Dynamic London


Since launching the data store in early 2010, the Ordnance Survey have been releasing a number of updates to an interesting dataset – VectorMap District – which is a generalisation and simplification of their MasterMap “gold standard” dataset for Great Britain. The updates have been appearing roughly every 6-12 months, and by comparing them in a GIS, you can start to see how places change – at least in the eyes of the Ordnance Survey surveyors tasked to keep the map current. Roads occasionally get built, but building footprints evolve more rapidly – as office blocks and housing developments get taken down and rebuilt with higher capacities or more glass windows.

I’ve taken three of the VectorMap District dataset releases – April 2012, September 2013 and March 2014 – combined the data together and used QGIS’s layer compositing operations to show the geographical differences.

The colours tell of the age of the building – bearing in mind that there is a lag of a few months or years between buildings appearing/disappearing in real life, and on the map. For example, the Olympic Stadium, the turquoise oval above, appears in the 2013 dataset but not the 2012 one, even though of course it was finished in 2011, for the London 2012 Olympic Games.

White Building has existed throughout the three years.
Red Building existed in 2012 only (see note below about extra detail).
Purple Building existed in 2012-2013, but has now gone.
Blue Building was new for 2013, but has now gone.
Turquoise Building was new for 2013, still present (see note below about extra detail).
Green Building is new for 2014, still present.
Yellow Building was around in 2012, disappeared in 2013, but has appeared again now.
Black No building existed in any of the three years.

Above, much of the Olympic Park can be seen – the permanent new buildings (turquoise), temporary buildings for the Games only (blue) and demolished for the games and associated planned development (red). Below, the map covering a wider part of London, zones of activity can be seen. For example, demolition associated with the Nine Elms and Deptford Creek developments (red), and major new blocks such as near the Arsenel stadium (yellow).

Important Note

Between the 2012 and 2013 datasets, the Ordnance Survey changed they way they applied the generalisation on the data, so some of the 2012-2013 changes (shown as red on the maps here for reductions, and turquoise for additions) are as a result of this. For example, narrow gaps between buildings, that always existed, are shown for the first time in 2013 in red (building reductions).

As such, my map slightly overemphasises changes between 2012 and 2013. For example, the pitch at Arsenal and the Great Court at the British Museum appear as changes, but they were always there. As a rough rule of thumb, the smaller red/turquoise patches are due to the generalisation changes, the larger areas of colour show genuine change. With this important caveat, the map remains an interesting insight into London changes, and the larger coloured regions give a good indication of parts of London which are undergoing intensive building redevelopment.

The Bigger Picture

Here is the map for central London – click on it to see a full-size version.


37000 Old OS Maps


The National Library of Scotland (NLS) yesterday unveiled a HUGE collection of maps that they have digitised and placed online. The maps, covering England and Wales, are historic Ordnance Survey maps that are between 60 and 170 years old and are at a high resolution. The scale is 6-inch-to-the-mile and covers the whole country. At the moment each map can be viewed by clicking on the appropriate box on an online map, they plan to undertake further work to join many of the maps together to create a single scrollable historic map of the whole country this summer.

The extract above, of the Kew Bridge area in 1899, is from this map (I’ve shifted the white balance.) Some of the maps have some rather nice colouring for water – with the blue colour being augmented by some subtle shading on the riverbanks. The same effect is see in a Snowdon map (extract below), from 1889.

I featured an earlier release of Victorian 60-inch-to-the-mile maps, for London, on Mapping London. The number of retweets and Facebook likes for this posting was unprecedented for the blog, suggesting a huge interest in high quality scans of historic maps.

Here’s their press release, includes the reason why the NLS is including maps from outside Scotland!

New map resource – OS six-inch England and Wales, 1842-1952

We are very pleased to announce the availability of a new website resource – zoomable colour images of the Ordnance Survey’s six-inch to the mile (1:10,560) mapping of England and Wales. All our map digitisation work in recent years has been externally funded, hence the recent expansion of our map images beyond Scotland.

This is the most detailed OS topographic mapping covering all of England and Wales from the 1840s to the 1950s. It was revised for the whole country twice between 1842-1893 and between 1891-1914, and then updated regularly for urban or rapidly changing areas from 1914 to the 1940s. Our holdings are made up of 37,390 sheets, including 35,124 quarter sheets, and 2,237 full sheets.

The maps are immensely valuable for local and family history, allowing most features in the landscape to be shown. The more detailed 25 inch to the mile (or 1:2,500) maps allow specific features to be seen more clearly in urban areas, as well as greater detail for buildings and railways. However, most topographic features on the 25 inch to the mile maps are in fact also shown on the six-inch to the mile maps.

The easiest way of finding sheets is through a clickable graphic index using our ‘Find by Place’ viewer:

This allows searching through a gazetteer of placenames, street names, postcodes and Grid References, as well as by zooming in on an area of interest with smaller-scale locational mapping as a backdrop.

The sheets are also available via county lists:

We plan to also make georeferenced mosaics available of the series by the late summer.

OS six-inch England and Wales home page:

Further information: