Are map legends too lazy?

A somewhat click-baity blog title, but I wanted to crowdsource some knowledge from proper carto/viz people, so if you have any insights on what I write, please feel free to get in touch via twitter or e-mail. No doubt what I write about below already has a name but I don't know what that is and I haven't seen this functionality in proprietary or open source GIS. By asking 'are map legends too lazy', what I really mean is are GIS-made choropleth map legends doing enough for us in their current form - and is there an opportunity for us to add some new functionality which enhances the communicative power of the humble choropleth legend? An example... look at the map below, which I created in QGIS. It's a map of a new deprivation* dataset for England, focused on the local authority of Birmingham.

Deprivation choropleth, with legend and inset map

This dataset is typically understood and discussed in terms of deciles, hence the classification used above. The dataset goes from decile 1 (most deprived) to decile 10 (least deprived) - within the context of England as a whole. Cities like Birmingham tend to have a higher proportion of their small areas in the most deprived decile, and in map form this results in lots of red and not much blue, as you can see above. If you wanted to find out how many areas were in decile 1 (most deprived) you would know that it was 'a lot' but because the inner-urban areas tend to be smaller in size (relative to the blue ones), making an accurate assessment visually is quite difficult. In fact, owing to the different sizes of the spatial units, you could quite easily take the wrong message away from a choropleth like this.

My solution? Make the legend do more work. Make it tell us not just what the colours represent but also what proportion of areas are in each category by scaling the colour patches relative to the proportion of areas in each choropleth class - in the form of a bar chart - what I call a 'bargend' (jump in at this point if you already have a name for this). You could, without much effort, add in a table or a separate chart, but I want the legend to actually be the bar chart. In part, I was inspired to attempt this in QGIS because of Andy Tice's prototype scatterplot layout and his comment that he'd like to get it working in the QGIS Atlas tool. Here are some results, followed by further thoughts.

This time, I've added in a 'bargend'

A closer look at the bargend for Birmingham

When I do a visual comparison of the Birmingham map, I'm surprised that the least deprived (i.e. richest) areas only account for 1.7% of the total, because I'm drawn to the blue of the choropleth. This could be solved though a cartogram approach, but I wanted to preserve geographical accuracy here. I'm not surprised that almost 40% of areas are in the poorest decile - that's what I'd expect from what I know about deprivation in English inner-cities. Let's look at another example below.

The London Borough of Tower Hamlets

This time I've shown one of the poorest parts of London - Tower Hamlets. An interesting aside here is the emergence of one area in decile 9 (i.e. richer area) compared to the pattern from 2010. This is almost certainly linked to gentrification and displacement rather than individuals becoming 'less deprived'. I find the extra information provided in the bargend very useful analytically/cognitively compared to the simple legend we would normally use.

Now let's look at a few more...

Liverpool contains relatively few 'non-deprived' areas

Like Liverpool, Manchester has many poor areas

Middlesbrough has the highest % in the most deprived decile

One of the benefits of this approach, in my view, is when you compare different places - you can click on an image above and then go forward and backward to make comparisons. The added value of the bargend approach means that you have precise details of the proportion of areas in each decile and you can make more meaningful comparisons. You could just do this with a table or chart and dispense with the map altogether, but then you'd lose the very important ability to identify where precisely individual areas are and where spatial concentrations of deprivation (and affluence) exist. Talking of affluence, it's only fair that I show you some maps of places that are at the opposite end of the scale. Two prime examples...

A beautiful part of the world, but very blue

Hart, you almost broke my chart (highest % in decile 10)

I'll wrap up with a few points.

1. I'd love it if someone could find a way to add in this functionality natively in QGIS. I had to do a bit of thinking and tinkering to automate this in the Atlas tool, but I now have it working well and everything dynamically updates and re-positions itself once you set it up.

2. I wouldn't always want to use a bargend, but I think it's something that adds value without taking up much more space (if any) in map layouts.

3. I'm trying to think of any drawbacks of this approach, but I can't. I'm happy for others to chip in with ideas on this.

4. I think 'bargend' is a terrible word. Please tell me it already has a nice sounding name, or invent one for me. [update: in my rush to coin a phrase, and because I was mapping deciles as categories - as in a bar chart - I was thinking about bar charts rather than histograms. This is really a histogram but it uses named categories (deciles) which in theory could be re-ordered and the chart would still make sense, so perhaps the bargend retains qualities of both and, anyway, a histogram still uses bars]

5. Are map legends too lazy? Not really, but they can sometimes work harder.


Addendum
Andrew Wheeler very kindly got in touch to share a few relevant papers on the subject. The Kumar paper is very close to what I propose (though he does the chart for the entire dataset rather than a subset) and he calls it a 'Frequency Histogram Legend' - more accurate perhaps, but less catchy. The Dykes et al. paper is very interesting and I like the treemap approach.

Hannes (@cartocalypse) also got in touch to say he likes the idea and he's suggested 'legumns', which is also useful (but more difficult to pronounce!).

I'll add more on the topic if people respond.

* Just in case the use of this word sounds odd to you, we use the word 'deprivation' in the British context in studies of urban poverty/disadvantage but it's not exactly the same thing. I've written about this in previous academic papers but to all intents and purposes more deprived means 'poorer' and less deprived means 'richer'. In the maps above, you could say red: poor and blue: rich and you wouldn't be wrong (ecological fallacies notwithstanding).

Divided Cities?

There has been a lot of talk recently about the links between deprivation and the recent riots in England (e.g. this from the Guardian), and since I'm interested in the issue I thought I would do some very basic analysis to look at the spatial divisions of deprivation in English cities. To do this I took London, the eight core cities plus Bradford, Coventry, Hull and Leicester and mapped the 10% most (in red) and 10% least (in blue) deprived (IMD 2010) areas on one large map graphic. The results are shown below - click the image to enlarge.

Some interesting comparisons can be made from this image. In Hull, Leicester, Manchester and Nottingham there are no areas amongst England's 10% most deprived and in Liverpool there is only one. In Sheffield there is a clear NE/SW split in terms of the location of the most and least deprived and in Manchester, Birmingham and Liverpool the number of areas in the most deprived 10% is quite high.

Not much else to say now except that I think in studies of deprivation we should perhaps be more concerned with inequalities and how they manifest themselves spatially.

Comparing Deprivation in the English Core Cities

The eight English core cities outside London often work together on issues that affect them. One thing I've been looking at recently on some work in Sheffield is how the core cities compare in terms of patterns of deprivation. So, I've mapped and compared them below. Red areas are amongst the 10% most deprived in England and the darkest blue areas are amongst the 10% least deprived in England. The maps are at different scales but the point here is just to provide a quick visual comparison of cities in this map matrix view.

IMD 2004, 2007, 2010 - Change Over Time...

This is the last deprivation-related post in a while. I promise. I've been experimenting with English IMD data from 2004, 2007 and 2010 and have finally found a moment to do to it what I did to the Scottish IMD data, where it is all online and clicking an area produces a chart showing change in rank through time. In this new example, I've also added in the change in rank between 2004 and 2010, as you can see below. The rank change doesn't tell you if an area became more deprived in absolute terms, only that it is less or more deprived in relation to the other 32,481 LSOAs in England.

You can see in the example above a case of a quite large change in rank. This is for an LSOA in London. The maps are quite basic because I don't have time right now to build a more sophisticated site but the colour scheme is based on quintiles, with red most deprived, yellow next and blues less deprived (the darker the blue, the less deprived), as below.

For the moment, I don't have any plans to extend this analysis to Wales or Northern Ireland, but you never know...

Employment Deprivation in England and Scotland

I'm doing a talk today at the Employment Research Institute in Edinburgh, so I thought I'd put my presentation here and also post a few nuggets of information. Employment deprivation is the subject of the talk and I've been exploring spatial patterns in relation to this. What is 'employment deprivation'? It's involuntary exclusion from the labour force.

A few bits of information... Out of the different indices of deprivation for England and Scotland between 2004 and 2010, the highest figure for employment deprivation was in a LSOA in Rochdale, with 75% of people out of work. The highest figure in Scotland was a Data Zone in Glasgow in 2004 and one in Edinburgh in 2006 with 65% of working age people not in work. There are no major surprises in the general spatial pattens but there are interesting findings in the spatial analysis in the presentation (well, that's what I think).