Here’s a little visualisation created with the DataShine platform. It’s the DataShine Commute map, adapted to show online cycle flows, but all of them at once – so you don’t need to click on a location to see the flow lines. I’ve also added colour to show direction. Flows in both directions will “cancel out” the colour, so you’ll see grey.

London sees a characteristic flow into the centre, while other cities, like Oxford, Cambridge, York and Hull, see flows throughout the city. Other cities are notable for their student flows, typically to campus from the nearby town, such as Lancaster and Norwich. The map doesn’t show intra-zone (i.e. short distance) flows, or ones where there are fewer than 25 cyclists (13 in Scotland as the zone populations are half those in England/Wales) going between each origin/destination zone pair – approximately 0.15% of the combined population.

Visit the Great British Bike to Work Map.

If you are looking to name-check DataShine in a paper, then we now have the citation for you! Interactive mapping for large, open demographic data sets using familiar geographical features is our paper (authors Oliver O’Brien and James Cheshire), published in Journal of Maps, as open access, so you can read it right now.  

To cite DataShine or this paper, please use:

Oliver O’Brien & James Cheshire (2016) Interactive mapping for large, open demographic data sets using familiar geographical features, Journal of Maps, 12:4, 676-683, DOI: 10.1080/17445647.2015.1060183

There is also a paper published by a colleague at the UCL Centre for Advanced Spatial Analysis (CASA), Duncan Smith – Online interactive thematic mapping: Applications and techniques for socio-economic research. It’s published in Computers, Environment and Urban Systems. Duncan reviews DataShine as well as a number of other ways of mapping large demographic datasets. It includes statistics on our user-base that you won’t have seen announced/published anywhere else. This paper is also open access. 

DataShine Census has won the British Cartography Society’s Avenza Award for Electronic Mapping, for 2015. The glass trophy and certificate were presented to DataShine creator Oliver O’Brien at the award ceremony and gala dinner for the combined BCS/Society of Cartographers conference “Mapping Together” which took place in York, earlier this September. The prize was presented by Peter Jones MBE, the BCS President.

Additionally, DataShine Election was Highly Commended for the Google Award for mapping of the UK General Election 2015.

The book “London: The Information Capital” which DataShine PI James Cheshire co-authored with Oliver Uberti, won three awards at the same ceremony, the Stanfords Award for Printed Mapping, the John C. Bartholomew Award for Thematic Mapping (for Chapter 3 of the book), and the meeting’s grand prize, the BCS Trophy. Dr Cheshire was on hand to receive the trophies and certificates.

The awards cap a successful year for the DataShine project which has seen hundreds of thousands of viewers, several key media articles and four key websites launched, along with a number of variants, most recently including DataShine Scotland Commute which was commissioned by the National Records of Scotland. Full details of the project can be found on the project blog.

The Borders Railway opened last week – a 30 mile new railway running between Edinburgh and the Scottish Borders, which was for the last fifty years the largest populated region in the UK without a railway connection. The railway largely follows the route of the Waverley Line, which used to connect Edinburgh to Carlisle via the Borders (Galashiels and Hawick) before it was axed in the 1960s. The new line is highlighted in the map above, as are the small stations that lead to central Edinburgh at the end of the route. Galashiels is the penultimate station, with Tweedbank, a suburb of Galashiels, being the terminus.

This post looks at the pre-opening (i.e. Census 2011) commuting patterns between the western Borders towns, both those now connected to the new railway and those nearby but not linked, and Edinburgh. It’s a commute pattern that is personally interesting to me as I have childhood memories of waiting various “Munros” buses to come over the hills from the Borders, to get into Edinburgh. It also looks at the relative deprivation scores and potentially related census characteristics, between the towns.

The graphics here are from our newly launched DataShine Scotland Commute which shows travel-to-work flows as straight-line origin/destination maps – it should be noted they don’t include any transnational commutes, e.g. from the Borders towns into Carlisle in England. For that, you need the DataShine Region Commute. The Borders council area is shaded in blue.

Here are four maps from towns (and surrounding areas), in the Borders region, near each other and approximately equidistant to Edinburgh, showing the flows out to work from people living in those areas (red) and flows in to work there from people living outside (blue). The four places I’m showing are, from west to east, Peebles, Innerleithen, Galashiels and Earlston.

Peebles:

Innerleithen:

Galashiels North:

Earlston & surrounding area:

Notice the odd one out?

Galashiels, the one of the four to which the new Borders Railway now connects Edinburgh to shows a significantly smaller level of commuting activity (as of the 2011 Census) to Edinburgh, than the other three areas, all statistical areas having approximately equal populations, and which are approximately the same distance from Edinburgh (an 45-minute drive or an hour-long bus journey). You can see an interactive version for all four, and indeed anywhere else in Scotland, here. Instead, it shows most commuting activity remaining within the Border valleys. It has a much weaker social connection to Scotland’s capital city.

N.B. In Earlston’s case, the small population in the town means that local rural communities are also included in its data point. Conversely, Galashiels’ large population is split into three, I’ve chosen the northern one which is a little closer to Edinburgh and also likely includes Stow, a small village to the north and the next stop on the new Borders Railway. The other Galashiels areas are broadly similar in terms of their commuting patterns.

Looking at DataShine Scotland which looks instead at the “static” aggregated small-area Census data rather than the flows above, we can begin to understand more about the demographics of each of the four towns and why Galashiels Edinburgh links are weaker. Galashiels’ population is generally younger and less likely to be married. People of Galashiels are also less likely to declare themselves of being of “very good health” than the other three areas. A younger, less healthy population is potentially indicative of a more deprived area, this is confirmed via mapping the Scottish Index of Multiple Deprivation (SIMD) 2011. Galashiels has both more and less deprived areas (red and green), but stands out against Peebles, Innerleithen and Earlston, who are generally green/yellow and so do not have a significant deprived area.

So perhaps poorer Galashiels, having little existing interaction with affluent Edinburgh, stands more to gain from the new, regular and fast connection to Scotland’s capital city, and that the new connection will, if it is used well, likely have a significant social impact on Galashiels and its fortunes, more so than would be gained from improving the other Borders towns. Renewed railway lines, as a method to transform the wealth of areas, is likely demonstrated by the dramatic effect on fortunes and house prices along the East London line in north-east London, following the reinstatement of the railway line there, and perhaps Galashiels and the surrounding areas will also see a step-change in the years to come?

Map data Copyright OpenStreetMap contributors 2015. Census data from NRS, Crown Copyright and database right 2015.

We’ve made three changes to the DataShine Commute websites:

1. For DataShine Scotland Commute we have made use of a new table, WU03BSC_IZ2011_Scotland, published recently on the Scotland’s Census website, which breaks out small-area journeys by mode of transport, in the same way that the England/Wales data does.
   The small-area geography used, Intermediate Geography “IG”, is broadly equivalent to the MSOAs used in England/Wales although the average population is half the size, so we show the lines twice as thickly. There is some additional grouping in the Scotland data – metro services (i.e. Glasgow’s Clockwork Orange) are combined with rail, and commutes by taxi and motorbike are moved into “Other”.

   Looking at the data reveals some characteristic patterns which might be expected, for example, on the edge of Edinburgh, the commute to that point is from outside of the city, and from that point to closer in to the city centre. This effect is strongly also seen around London.

2. For DataShine Commute (England/Wales) we now include numbers, in the summary table for each area, for commuters living in that area who work in Scotland, in Northern Ireland, outside the UK, at home, in no fixed location or on offshore installations.
   These numbers, along with those for people who work elsewhere within the area, are shown in grey in the table. None of these seven special categories are shown as lines on the map.

3. Finally, we have expanded and renamed, to DataShine Region Commute, the previous map of commuting flows in Scotland which we introduced last month alongside DataShine Scotland. The previous map was at a coarse level (showing only flows between local authorities) and was intended to be a stop-gap until the above more granular data was released. Rather than removing this website, we have decided to expand it to include the data from England, Wales and Northern Ireland too, and show flows between these places as well. This was generally straightforward to do as the Office for National Statistics published a UK-wide table at local-authority level. Constructing the Northern Ireland part of the map was less trivial as the local authority boundary files there are not straightforward to obtain, and needed to be derived.For visual clarity, we have colour coded the different nations within the UK.

We have also taken the opportunity to upgrade almost all the DataShine websites (see list on the left!) to use OpenLayers 3.8.2, the most recent release of the rapidly evolving mapping library. The new version has a lot of changes, which we’ve tried to work with (such as ol.format.TopoJSON() and ol.View.fit()) but may have missed something, so if you see any new bugs on a DataShine website let us know in the comments.

The latest web map in the DataShine website portfolio is DataShine Election, which shows results from the 2015 and 2010 UK General Elections.

You can view the interactive map or read more about it.

This post is cross-posted from oobrien.com.

OpenLayers is a powerful web mapping API that DataShine uses to display full-page “slippy” maps. DataShine: Census has recently been upgraded to use OpenLayers 3. Previously it was powered by OpenLayers 2, so it doesn’t sound like a major change, but OL3 is a major rewrite and as such it was quite an effort to migrate to it. In due course, we plan to migrate the other DataShine websites to OpenLayers 3 too, although each website is quite different under the covers so will prove a challenege too.

Here are some new features, now in DataShine, which are made possible by the move to OpenLayers 3.

Drag-and-drop shapes

One of the nicest new features of OL3 is drag-and-dropping of KMLs, GeoJSONs and other geo-data files onto the map (simple example). This adds the features pans and zooms the map to the appropriate area. This is likely most useful for showing political/administrative boundaries, allowing for easier visual comparisons. For example, download and drag this file onto DataShine to see the GLA boundary appear. New buttons at the bottom allow for removal or opacity variation of the overlay files. If the added features include a “name” tag this appears on the key on the left, as you “mouse over” them. I modified the simple example to keep track of files added in this way, in an ol.layer.Group, initially empty when added to the map during initialisation. Note: The file you drag on should be in WGS84 lat/lon format (aka EPSG:4326) or WebMercator (aka ESPG:900913 or EPSG:3857). It will not work with the British National Grid projection (aka EPSG:27700).

Nice printing

Another key feature of OL3 that I was keen to make use of is much better looking printing of the map. With the updated library, this required only a few tweaks to CSS. Choosing the “background colours” option when printing is recommended. Printing also hides a couple of the panels you see on the website.

Better looking controls and smoother zooming

OL3 also has much smoother zooming, and nicer looking controls. Try moving the slider on the bottom right up and down, to see the smooth zooming effect. The scale control also changes smoothly. Finally, data attributes and credits are now contained in an expandable control on the bottom left.

A bonus update, unrelated to OL3, is that I’ve recreated the placename labels with the same font as the DataShine UI, Cabin Condensed. The previous font I was using was a bit ugly.

For notes on UTF Grids and Permalinks, both of which required a lot of work to reimplement so that DataShine with OL3 behaves like the version with OL2, see the developer blog post.

Above: GeoJSON-format datafiles for tube lines and stations (both in blue), added onto a DataShine map of commuters (% by tube) in south London.

One of the more spatially interesting datasets on DataShine: Census is about central heating – do houses have it, and what is the fuel source? The table is QS415EW and here’s what one of the categories – proportion of houses with gas central heating – looks like on DataShine (and above). You’ll notice a distinctive pattern, with city centres and the countryside having low proportions of houses with gas central heating (yellow), while city suburbs and towns have much higher proportions (red). City centres may have these low values because they contain either very old houses (which never had it) – and/or very new houses that use more modern forms of central heating, are much more highly insulated, or are blocks of flats where gas central heating systems are perhaps considered dangerous now. In rural areas, some of these places maybe never had a connection to the gas main anyway. It’s the city suburbs, the big expansion of the 60s/70s, where gas central heating was always put in by default.

Oil central heating, being more expensive, is rare in urban areas but a practical necessity in the countryside, such as in rural Wales. Solid fuel is popular in Northumberland.

Barrow Island in the Lake District and Aberdaron in Wales are the two wards that have the highest proportion of households with no central heating at all. Many of the houses in the area at least are holiday houses, which are presumably mostly populated in the summer. It could be a bit chilly there in the winter!

See the live map on DataShine now. Change the fuel type at the No. 3 drop-down on the top right.

Local Area Rescaling

This helps draw out demographic versions in the current view. You may be in a region where a particular demographic has very low (or high) values compared to the national average, but because the colour breakout is based on the national average, local variation may not be shown clearly. Clicking on the “Rescale for current view” button on the key, will recolour for the current view.

For example, the popularity of London’s underground network with its large population, means that, for other cities with metros or trams, their usage is harder to pick out. So, in Birmingham, the Midland Metro can be hard to spot (interactive version):

Upon rescaling, just the local results are used when calculating the average and standard deviation, allowing usage variations along the line to be more clearly seen:

As another example, rescaling can help “smooth” the colours for measures which have a nationally very small count, but locally high numbers – it can remove the “speckle” effect caused by single counts, and help focus on genuinely high values within a small area.

Hebrew speakers in Stamford Hill, north-east London (interactive version):

Upon rescaling, a truer indication of the shape of the core Hebrew-speaking community there can be seen:

Occasionally, the local average/standard deviation values will mean that the colour breakout (or “binning”) adopts a different strategy. This may actually make the local view worse, not better – so click “Reset” to restore the normal colour breakout. Planning/zooming the map will retain the current colour breakout. PDFs created of the current view also include the rescaled colours.

Data Download

On clicking the new “Data” button on the bottom toolbar, you can now download a CSV file containing the census data used in the current view. Like the local area rescaling functionality, this data download includes all output areas (or wards, if zoomed out) in your current view. This file includes geography codes, so can be combined with the relevant geographical shapefiles to recreate views in GIS software such as QGIS.

Next on the DataShine project, we are looking to integrate further datasets – either aggregating certain census ones or including non-census ones such as IMD and IDACI deprivation measures, or pollution.

The 2011 Area Classification for Output Areas, or 2011 OAC, is a geodemographic classification that was developed by Dr Chris Gale during his Ph.D at UCL Geography over the last few years, in close conjunction with the Office for National Statistics, who have endorsed it and adopted it as their official classification and who collected and provided the data behind the classification – namely the 2011 Census.

A geodemographic classification such as this takes the datasets and looks for clusters, where particular places have similar characteristics across many of the variables. It does this on a non-geographic basis, but spatial autocorrelation means that geographic groupings do typically appear – e.g. a particular part of an inner city will typically have more in common with another part of the inner city, than of the suburbs. However, these areas will often also share much in common with other “inner city” parts of cities elsewhere. Names are then assigned, to attempt to succinctly describe the clusters.

As part of the DataShine project, we have taken the classifications, and mapped them, using the DataShine style of restricting the classification colouring to built up areas and (when zoomed in) individual rows of houses. The map is the third DataShine output, following maps of individual census tables and also the new Travel to Work Flows table.

We’re just mapping the eight “Supergroups”, the top-level clusters. A pop-up shows the more detailed groups and subgroups, and you can find pen-portraits for all these classifications on the ONS website.

Click on the box for an individual supergroup, in the key at the top, to see a map showing just that supergroup on its own. For example, here are the “Cosmopolitan” dwellers of London:

Like 2011 OAC itself, the map covers all of the UK, including Scotland and Northern Ireland. For the latter, there is no Ordnance Survey Open Data which is how I created the building/urban outlines, so I have improvised with data from OpenStreetMap and NISRA (Northern Ireland Statistics).

The map is part of DataShine, an output of the BODMAS project, but also is in conjunction with the the new Consumer Research Data Centre, an ESRC Data Investment which is being set up here at UCL and other institutions. As such, there is a CDRC version of the map.

As part of the BODMAS project we have also been studying the quality of fit of 2011 OAC for different parts of the UK, and techniques to visualise the uncertainty and quality of the classifications. We will be presenting these findings at the Uncertainty workshop at the GIScience conference in Vienna, later this month.

Direct link to the map.