Sunday, 1 March 2015

Static maps from CartoDB

I've used CartoDB quite a bit recently to create interactive maps. My Historic Buildings of Scotland map and my England Grade I Listed Buildings map have both proved popular. Another map that received lots of traffic was my English Greenbelt map. This one in particular was popular I think because it allowed people to find out exactly where the green belt was near them - or in fact to find out which areas aren't greenbelt. Although interactive maps are great for some things, sometimes we just want to put a static image in a report or on a website and this can often be a bit tricky with interactive content. Luckily, CartoDB allows users to export static image files (png format), and you can customise the dimensions as well. This CartoDB blog post shows you how - though you may need to disable pop-ups in your browser if it doesn't work. This tool opens up the static map in a new browser tab so if nothing happens check your pop-up blocker settings.

View the full size version

As you can see above, I've exported my English Greenbelt map. I often get asked if I have a large static image of it because if you search online there aren't really any large up to date, decent resolution, detailed green belt images. I also added some attribution information (important!) and a basic title. This is just a quick example so isn't perfect in relation to labels and so on but it gives a simple overview of the green belt.


Sunday, 25 January 2015

How many people live in Tokyo?

Back in August 2014 when I was preparing some material for teaching students how to query data in a GIS, I devised a very simple example where they had to select all the countries in the world with a lower population than the Tokyo Metropolitan Area (or Greater Tokyo Area, as it's sometimes called). I did this just as an example but since I found the results quite interesting, I quickly turned it into a map and posted it on Twitter, complete with 'Toyko' typo in the subheading. It was really just a quick example of how to query data in a GIS but it also highlighted the massive population of the Tokyo metropolitan area. You can find the full size, original image here. I was prompted to write this blog because the map was reposted on the Canadian Twitter feed @AsapSCIENCE a few days ago and since then my inbox has been a bit busy.


Now, back to the original question of how many people live in 'Tokyo'. Well, when I say 'Tokyo' in the map, I'm referring to the wider metropolitan area, which in 2014 the United Nations World Urbanization Prospects said has a total population of 37,833,000, far beyond the next largest urban agglomeration, Delhi, at 24,953,000. By way of comparison, using the urban agglomeration population (rather than a city's administrative boundaries), Toronto had 5.9 million, London had 10.2 million and Beijing had 19.5 million. Clearly, these definitions include other urban places that are not Tokyo (e.g. Yokohama) but they are recognized as being part of a fairly coherent metropolitan area. 

So, in the map, I'm using a figure of 'approx 36 million' as I say but in reality the UN figure is a bit higher. If we want to narrow the definition down to just the inner urban area then obviously the figure reduces significantly. I'm not usually one to cite Wikipedia, but in this case it's a good place to go to learn about the various definitions of the Greater Tokyo Area/Kantō region. If you don't want to click, here's a summary of some Tokyo populations...
  • Former city area (23 'special wards') - 8.95 million people
  • Tokyo Metropolis - 13.05 million
  • Tokyo Metropolitan Employment Area - 31.70 million
  • National Capital Region - 43.47 million
If we take the metropolitan definition used by the United Nations, then Tokyo does indeed have more people than Canada, at 35.5 million (2014 Statistics Canada estimate) and far more than Australia at 23.7 million (see their population clock). The Tokyo metropolitan population is roughly the same as the whole of California, which currently stands at about 38 million.

Link to chart

This was all just a little bit of map trivia and whilst it seems to have annoyed some people who live in a red country, the point was just to demonstrate the simple analytical power of GIS in addition to the size of Tokyo (to make it more interesting). The data I used are from Natural Earth if you want to have a look yourself and the software I used is a free GIS called QGIS, which is really good. Some other random facts about what happened to my original tweet...

Tokyo metropolitan area


Saturday, 17 January 2015

Interpreting political maps

I recently tweeted a couple of maps showing the 100 most and least deprived constituencies in England. I used the 2010 English Indices of Deprivation to calculate this, aggregating the data from smaller areas to parliamentary constituencies. The method is not perfect but on the whole the areas identified are either among the poorest or richest in England. There are 533 constituencies in England so the figure of 100 is roughly the 20% most and least deprived (18.76% to be more precise). I shaded the maps using red for Labour, blue for Conservative, Yellow for Liberal Democrats and so on. The most obvious thing about the maps is, of course, the fact that the most deprived map shows nearly all Labour constituencies and the least deprived shows almost all Conservative constituencies. Click the caption below the images to see interactive versions.

100 most deprived constituencies


100 least deprived constituencies

These kinds of maps often make a big impact and are shared widely but people tend to draw conclusions from the patterns they see that are not necessarily correct - and often conclusion which mirror pre-existing biases and perspectives. For example, some people see these maps and claim that voting Labour makes you poor or that only rich people vote Conservative. Some would even claim that this proves that Labour has failed the constituencies they serve. Opponents would argue that coalition cuts have merely deepened spatial inequalities and hit Labour-voting areas hardest. This is all a bit dramatic, but you don't have to search online long to find such views.

Other people might say that if you want to be richer you should vote Conservative. Other people would tell you not to be so simplistic and point to the way in which voting patterns are formed at the local level. Still others might point to the longstanding economic differences between north and south in England and say that this has something to do with it. Perhaps others will say that the Conservatives are the party of the rich and that Labour are the party for the poor. There are varying degrees of truth in all these views but the point I want to make here is that none of this can be proven just by looking at a political map.

For me, such maps are a starting point for a conversation about what these patterns might mean, whether they are a problem and what might be done about it, if anything. I'm not making these maps because I'm pro-Labour or pro-Conservative or because I think that they prove anything in particular but because I want to draw attention to the patterns and what they might mean. Finally, some observations from the maps...


  • There are no Labour constituencies amongst the 100 least deprived in England.
  • There are 2 Conservative constituencies amongst the 100 most deprived in England.
  • Sheffield Hallam (Liberal Democrat, Nick Clegg) is amongst the 100 least deprived constituencies in England. 
  • There are 5 Liberal Democrat constituencies amongst the 100 most deprived in England.
  • There are 7 Liberal Democrat constituencies amongst the 100 least deprived in England.

It will be very interesting to see how these patterns change (if at all) after the General Election this year.


Wednesday, 14 January 2015

Visualising Residential Mobility in Urban England

Last year I produced a few commuting maps of England and Wales after the 2011 Census data were released. Now I've turned my attention to mapping patterns of residential mobility in urban areas of England as part of my work on understanding housing markets. This post highlights some of the patterns uncovered in the data - which are output area level migration flows for England and Wales (about 4 million individual flows). If you're interested in how I did this you can find out in a previous post. The first image is of the urban North West of England and for subsequent images I've zoomed to different parts of the country. I've kept it simple and only showed the flow lines, apart from in the North West where I've also added some place labels. It's all a bit experimental at this stage.

You can find a higher resolution image here


The North East of England


West, East and South Yorkshire


I think some places are missing (working on it)


I've adjusted the brightness a little to make this clearer

What does all this show? It shows what many people may already know or expect but basically it illustrates the extent of residential mobility patterns in some of England's major urban areas - plus a bit more in the South West example above. There's a lot more that could be said about this but for now I'll leave it at that. I'm sorry if your town or city isn't on the map! Maybe next time...


Notes: I've filtered the data so in certain cases some places are not shown (e.g. in the North West image places in North Wales are not visible). Also, I've only shown flows of a certain volume in order to filter out the noise. 

Tuesday, 23 December 2014

Simple Animations with QGIS - A Long Tutorial

This rather long post explains how you can make images and animations like the ones below using only open source software (mostly QGIS) and open data. I've just used flights from Hartsfield-Jackson International Airport in Atlanta because it's the busiest airport in the world and serves a wide variety of destinations. The methods are relatively straightforward but it does take time to get your head around if you're new to the software and the data. If you're already a competent QGIS user it should be pretty easy. Once you've got the method nailed down you can apply it to all kinds of different scenarios and datasets. First of all, though, here's a static image of outbound flights from ATL projected onto a globe.

Flight destinations from Atlanta (full size)

The first thing you'll need to do is get some data. In this example I've taken some general country boundaries from Natural Earth and for the flights data I've used the OpenFlights dataset. I then created an azimuthal orthographic projection centred on Hartsfield-Jackson International Airport in Atlanta. Hamish Campbell already wrote an excellent tutorial on how to do this in QGIS, so just follow that if you want to use a projection that makes your country layer appear as if it were on a globe. The only extra tips you'll perhaps need to replicate Hamish's method are as follows. To get the lat/long of a place you want to centre your projection on, just search for it on Google Maps and then look in the address bar for the coordinates. The python script on Hamish's page just needs to be copied and pasted into a text document, saved with a .py extension and then placed in the correct folder on your computer (on a PC with QGIS 2.4 it would be something like this: C:\Program Files\QGIS Chugiak\bin). Also, when you clip the layer to a global projection, QGIS will create a clip circle and a new clipped layer. These may not appear at all or if they do they'll maybe be very blocky. If so, go to the properties for the layer in QGIS and on the Rendering tab just untick 'Simplify Geometries'. I normally save new copies of these layers using Save As... from each layer.

A faster animated version of ATL outbound flights (big)

That should be your global projection sorted. You can then apply it via the Project menu in QGIS and then Project Properties... CRS and then 'Project on the fly' as in the tutorial.  To achieve some of the visual effects above, I just duplicated the clipped circle layer (you'll have to Save As... from the temp clipped layer to do this) and applied an inverted polygon style and a shapeburst fill with a blue to black gradient (Nyall Dawson did a great blog post on this, which you might find useful). I also did a similar thing with the land layer, just to make some of the smaller islands stand out. You'll also need to make the outline colour the same as the fill colour in the circle to avoid a line appearing through your earth.

A very slow version, with a pause at the end (big)

So far, so good. But what about the flight paths and animated dots? Well, to create the lines you can follow my blog post on flow mapping in QGIS and use the sample dataset I posted there. You'll need to calculate two new columns for this shapefile (see below) and use the MMQGIS plugin for QGIS (installed, as ever, via the Plugins menu). If you just add this file to your global azimuthal orthographic projection there will be so many lines and it may take a long time to display so there are a few intermediate steps I'd recommend... 

1. Open the new global flights dataset in a blank QGIS project using the default projection and then remove duplicate lines using Modify, Delete Duplicate Geometries in the MMQGIS plugin. Many routes (e.g. JFK-LHR) are served by multiple airlines and I wanted to only show origins and destinations. This also makes the file much smaller. 2. Although the flight connections would appear as straight lines on our global projection, I like them to look a little curved; partly for effect and partly to bring out the curvature of the earth but also because flight paths are not straight lines in reality. So, once I've removed duplicates I then 'densify' the lines in QGIS by adding in 50 intermediate vertices - done via Vector, Geometry Tools, Densify polygons in QGIS. 3. I then added this new flights layer to my ATL-based global projection and I clipped the layer using the Hamish Campbell method, and saving the resulting layer as a new shapefile. You should now have a globe centred on the location of your choice, plus some nice curved airline flight paths.

Same as above, but with labels and pause at end (big)

For the next stage, the way I did it was to open the dbf part of the new flight paths shapefile in Excel and then calculate an 'offset' lat and long column which I could use to animate the dots. You just need to read the Animate Columns part of Michael Minn's MMQGIS page to understand this. Once you've calculated the new lat/long offset columns you can save the csv. Once you've done this, import the csv into QGIS using the Add Delimited Text Layer (comma icon), using the airport origin lat/long as the x,y coordinates in the import dialogue. Filter the new layer so it only shows ATL origins and you'll just see one dot for ATL but actually there are many dots in the same location as they all have the same origin lat/long. Save the filtered layer as a new shapefile and then run the Animate Columns tool in MMQGIS using the appropriate fields and the number of animation frames you want (50 works well with this example). An important point here is that you need to make sure your QGIS window is quite small as the extracted image frames will be the same size as your QGIS map frame and if it's too big it will make a massive GIF.

You now have all you need to create an animation. There are many ways to create an animated GIF, but using GIMP is very simple. You can download this free, open source image manipulation programme in a few minutes. You then just need to go to File, Open as Layers and then select all the frames you just created in QGIS and GIMP will add them to the project and they'll appear in the Layers pane. You don't need to reorder them as they are numbered correctly from the MMQGIS export. From here you can go to File, Export and then select the GIF file format and use the animation options here. Try 50 milliseconds between frames as with 50 frames this will create a nice short 2.5 second animation that isn't too slow. You should use Filters, Animation, Optimize for GIF and then export from that window if you want a much smaller file size. I created another one of these visuals, centred on LHR and showing flows from JFK, LHR and PEK.

Same techniques, different data (and also a bit crazy)

That's quite a lot of information and quite a few steps but if you try this and still can't make it work feel free to get in touch via twitter or e-mail. Why would you want to do this? I'll leave that up to anyone who wants to try it but displaying movements of people and goods is relevant across a number of disciplines so hopefully some will find this useful.


Other tips and information: depending upon which location you're choosing, some of your lines or dots might be going the wrong way round the earth but you can fix this with a bit of simple maths in the offset calculation. In GIMP, you can add a different frame duration by adding a number and then ms to the layer info - e.g. 1000ms - so that it creates a pause effect, as in the examples above. I created an ATL point and a destination airports points layer from the imported csv so that I could manually create a couple of extra frames to add in to the end of my animation. One to show destinatinon airport names and the other just to label ATL. For the glow effect in the flow lines in the static image I used the Feature Blend 'addition' option in layer properties in QGIS. 

Acknowledgements: As ever, I've borrowed ideas and techniques from other QGIS users, including Hamish Campbell, Nyall Dawson, Nathan Woodrow. I decided to have a look at this after an e-mail exchange with Waldo Tobler about migration data. Thanks of course to the excellent OpenFlights team who make their data available under an Open Database Licence.

Wednesday, 17 December 2014

The Era of Radical Concrete meets Twitter

Back in 2008, when I moved into my office at the University of Sheffield, there were three mashed up boxes sitting under the desk. My curiosity got the better of me and I spent the next few hours looking through thousands of slides in hanging folders. This turned out to be the image archive of Jimmy James, the first Professor of Planning in our Department and former Chief Planner in England.

In the summer of 2013, after securing funding from the University of Sheffield Alumni Fund, I was able to hire Joe Carr and Philip Brown (recent graduates) to digitise the entire collection and upload it to our dedicated JR James Archive Flickr site. After more than 2 million views and a year online, the BBC magazine did a piece on the archive, called 'The era of radical concrete' which created huge interest and led to a follow up from residents of the places featured in the images. But one image in particular puzzled us... It was simply labelled 'Housing Scotland' (see below for the original slide and the online scanned image)  and we just couldn't fine an exact location for it, but we thought it was in the New Town of Cumbernauld, 15 miles or so from Glasgow.

Note the 'MAY 67' date stamp

Are you the boy? Let us know!
These images were used in teaching planners and urbanists for many years but some of them were not in the right place when we found the slides. We did well to track down the location of most images since between me, Philip and Joe we have a pretty good knowledge of most of the UK and we had thought the one above was of Cumbernauld, because that's where it appeared in the archive.

The New Towns section of the original archive

However, our interest was piqued when a commenter on Flickr said it definitely wasn't Cumbernauld. We tried a few times on Twitter, but to no avail. Even @MunicipalDreams couldn't help us. So, I tried one last time yesterday on Twitter and after 17 retweets we had suggestions that it was definitley NOT South Africa (!), but it might be 'somewhere in Cumbernauld', Falkirk, Dyce, Cwmbran, Killingworth, Peterlee or Saltcoats - among other places. I spent half the night looking round Craigavon, Crawley and Harlow, just to be sure. Nothing.

But this is where Twitter and the power of the crowd came to the fore. Actually, I should say this is when the determination of Michael Coates came to the fore, because ... wait for it ... he found where the photo was taken. It is in fact Abbots View in Haddington, East Lothian - as you can see from the embedded Street View image below. I've shown the 2008 Street View here as it is a better match for location and light but this is definitely it. The perspective of the Google cameras is a little different from Jimmy's original lens but it's a clear match so I declare Michael Coates winner of the Internet today and forever, though the Visual Resources Centre at Manchester Metropolitan University also deserve credit for joining the hunt.




So, that's one mystery solved. But what about the other mystery? Who is the boy on the bike?

By my estimation he'd be about 50 now and if anyone tracks him down we'll really be impressed. In the meantime, if you haven't already looked at the JR James Acrhive, what are you waiting for?


Thursday, 11 December 2014

Are you STILL here? Big Data's bad smell

Rambling introduction
Big data, big data, big data, big data. Yes, we're all probably sick to death of hearing the term 'big data' now* and somewhere along the lines the meaning has disappeared and whatever feelings the term originally engendered have now morphed into angst, disillusionment, embarrassment and general scoffing. Maybe this is because the famous 3 Vs of big data (vanity, vanity and vanity**) have not actually produced many good examples that can help explain why and how big data is useful. Actually, this kind of thing is pretty common in tech, academic, business and all sorts of other fields. A new concept comes along, people start jumping on the bandwagon, people start jumping off the bandwagon, bandwagon crashes and burns, and everyone says 'I was never on the bandwagon and, anyway, it was going in the wrong direction'. Despite the sarcasm and general rambling nature of this opening paragraph, below the floppy disk I'm going to make the case for not abandoning 'big data' at all - even if we do decide to stop using the term. I may also throw in even more bad metaphors, analogies and bad writing.

A 'big data' enabled storage device

Shark Jumping
In 2011 an article appeared on dbms2.com saying 'big data' had 'jumped the shark' (see below, and here for definition) and it makes for interesting reading because much of what it says is true. However, like most articles about big data, the core of the critique is not always directed towards the data or the analytical process but towards the hype. The comments section of this article is also very interesting because Doug Laney, the originator of the much-cited 3v concept in big data, has a few things to say. Fast forward to September 2014 and Techsling ask whether big data has jumped the shark (conclusion seems to be yes, probably), whereas in 2013 Wired said not to worry because big data had definitely not jumped the shark. However, my favourite article in this vein has to be the syncsort piece entitled 'has big data nuked the fridge', which actually contains a lot of common sense from a real 'big data' person. As for me, I completely agree that the hype has gone far too far. However, let's keep working on large datasets with powerful machines and then let people know when we get some useful or interesting results. And let's not use big data as an excuse for clever, anti-social people to avoid speaking to real people.

Big data didn't make it

Voices of reason
When you are immersed in hype, it's very important to find some voices of reason. Two prominent voices that I like the sound of are Rob Kitchin and David Lazer. Rob Kitchin is a professor at Maynooth University in Ireland and has written extensively on the need to approach big data sensibly and with a healthy degree of critique; most notably in his 2014 book The Data Revolution. My personal favourite is his piece from June 2014 entitled 'Big Data, new epistemologies and paradigm shifts' where he explores Anderson's 'end of theory' piece and argues both that big data is disruptive but also that there is 'an urgent need for wider critical reflection'. Rob's stance is particularly interesting from a social science perspective but actually I find his conclusions resonate much more widely.

My other voice of reason in big data is David Lazer, professor in Political Science and Computer and Information Science at Northeastern University and Visiting Scholar at the Kennedy School at Harvard, who wrote a great piece with colleagues on 'The Parable of Google Flu: Traps in Big Data Analysis' for Science in March 2014. Most people with an interest in big data probably know the story of Google Flu Trends because it made headlines for the wrong reasons in February 2013. Lazer et al. use this story to bring some reason to the big data debate and critique 'big data hubris'. Interestingly, they also talk about the need to incorporate 'small data':

"However, traditional “small data” often offer information that is not contained (or containable) in big data, and the very factors that have enabled big data are enabling more traditional data collection."

The conclusion of the Lazer et al. piece is not that we should abandon big data but rather that we need to understand what the recent data revolution means and then use innovative analytics to move towards a clearer understanding of our world.


Big dog, small dog
I have posted a photo below of a big dog and a small dog. They are both dogs. I can see why some people would get excited about big dogs. They can fetch bigger sticks. They can keep burglars at bay more easily and they are stronger, but they do take up more space. But the small dog has a really loud bark, can go places big dogs can't, knows just as much as the big dog and takes up less space in your house. They do require different approaches in relation to being looked after, but that's another issue altogether. Someone has even produced a nice visual representation of different kinds of big and small dogs.

They are both dogs


How to proceed
Apologies for the big dog, small dog nonsense above but I've been sucked in to the big data debate over the past few years and it always makes me think of this. I don't even have a dog. But I do have lots of data and a fancy computer and this is what I have in common with lots of other people who are 'doing big data'. So, to conclude and by way of trying to say something useful about big data, here's a final few bullet points...

  • Let's accept that the hype around big data has gone too far and put that to one side. It's not novel or useful to say that 'big data has jumped the shark', 'big data is all hype', 'big data is dead' or other similar comments. The people who are working with big data and have a critical mind (Kitchin, Lazer et al.) already know all this.
  • Let's try to take a more nuanced approach to understanding what big data is***, what it is not and what it can and cannot do - along the lines of what Kitchin refers to as a 'contextually nuanced epistemology'.
  • We ought to understand that the reason 'big data' emerged was because of enhanced processing power in computers which arrived at roughly the same time as access to very large datasets. This created the ability to ask questions of data that we previously could not answer because of problems of 'small tools'. But it hasn't really led to many transformative developments that people know about. This needs to change.
  • Let's start with big questions rather than big data. A very obvious point but the criticism that big data so far has been a solution in search of a problem is in some cases justified. 
  • Let's let the term 'big data' fade into the distance and keep working with large datasets and powerful computers on big societal challenges that we need to find the answers to (i.e. keep doing big data but stop calling it that).
  • Finally, let's keep in mind this statement form a Financial Times Magazine piece on big data from early 2014: "a theory-free analysis of mere correlations is inevitably fragile. If you have no idea what is behind a correlation, you have no idea what might cause that correlation to break down". 



* or 1 3, 5, 7, 10 years ago depending upon how far ahead of the curve you are.
** I think that's right...
*** data is, data are... I like is, even if some say it's wrong
This blog is written in a somewhat rambling, tongue-in-cheek style just to make a point