Why we didn’t use a cartogram in the Brexit map

Great Britain has voted to leave the E.U., and election result cartograms are all over the internet. However, for our map we decided to stick with a simple map instead.

Here are a couple of the cartograms I saw:

Source: The Guardian

Source: Buzzfeed

While these cartogram are undeniably pieces of beauty, there are still some significant problems.

Cartograms are too confusing for readers unfamiliar with the geography

Distorted geography works for people who know the undistorted geography in and out. But it is very hard to make sense of cartograms of countries you are not familiar with. First of all, we might not recognize the country’s outer shape, which might make some wonder what that thing is that we’re looking at. The same is true with regions within the country. It’s hard to point at Wales in both of the cartograms.

Reading and comparing areas is hard

Interpreting circles sizes is hard enough, but for irregular shapes like in the Guardian’s hexgrid cartogram this becomes even harder. I just doubt that by looking at the cartogram, anyone would correctly “guess” that the total vote was 52% for leave. That’s why, every map was accompanied by at least one bar chart showing the result.

Geographical area isn’t just “noise”

Geographical area is not just noise in a map. Especially with election maps, where regions are often roughly designed to contain similar population, the area tells us something important about the regions: population density. Which is an indicator whether some shape you’re looking at might be a city or a rural area. In cartograms, this information gets lost.

Cartograms are harder to label

As with all data visualization, labeling makes the difference! But with cartograms the boundaries between regions can disappear, which makes labeling a lot harder. The hexgrid cartogram does a better job of maintaining region outlines, but the distortion doesn’t help.

Where does Wales start and end?

There are other ways to address the same problem

Sometimes there are other solutions to address an uneven population distribution. In this case we added a simple table that breaks down the result by region, along with population figures for each. This is not as efficient to read, but it still tells you that London’s population is bigger than Scotland and Wales combined. Another idea was to use a simple map call-out to magnify the London area, both to give it more visual weight and to make it easier to see details inside the city.

Simple results table

London call-out

But wait, cartograms are still great!

This post is not meant to be a takedown of cartograms. They are useful and extremely powerful tools in data visualization, and for a lot more good examples I recommend Benjamin Hennings blog viewsoftheworld.net. Here, I just wanted to explain some of the problems cartograms face in news, which eventually led us to drop the idea.

When It’s Ok to Use Word Clouds

It’s ok to use word clouds if your goal is to encourage reading of a large set of otherwise unrelated words that are connected to one or two interesting values (and word count in a text doesn’t qualify as interesting).

This I tweeted yesterday and now I feel that if I encourage the (dangerous) use of word clouds, I have to explain this exception in a little more detail. Why is it sometimes ok to use a widely rejected visualization method, and most times not?

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Look, Ma, No More Mercator Tiles

Using open source tools it is now super easy to make your own map tiles, and with a little extra work you can render them in whatever map projection you want. No more excuses to use Mercator! For example, here is a map we published today at The Upshot. It shows where prime-age women are working more or less then average, and includes data from county-level in the overview map down to every census tract once you zoom in. And all is nicely projected in Albers Equal-Area Conic, a projection widely adopted as standard for U.S. maps.

here’s how you do it

Analyzing bias in opinion polls with R

Never trust a statistic that you
haven’t visualized yourself.

It’s election time in Germany and, as usual, there are tons of opinion polls telling us who is going to win the election anyway. It is debatable whether or not pre-election polls are healthy for our democracy in general, but at least everybody agrees that the polls should be kind of neutral. And if they are not, the institutes publishing the polls should be blamed publicly.

But how do we know if an institute publishes ‘biased’ polls? You guessed it: with data. More precisely: with data and the unique power of data visualization.
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Start using databases, today!

This post is written to welcome dataset, a new library to simplify working with databases in Python.

Let’s face it. Relational databases, such as MySQL, SQLite and PostgreSQL, are pretty cool – but nobody actually uses them. At least not in the day-to-day work with small to medium scale datasets. But why is that? Why do we see an awful lot of data stored in static files in CSV or JSON format, even though

  • they are hard to query (you need to write a custom script every time)
  • they are messy, as they cannot store meta data such as data types
  • it is a pain to update them incrementally, say if some record has changed

click to read the answer :)

Forget About Parties,
Visualize the Coalitions!

If I was asked for the golden rule of information visualization, it would be:

“Show the most important thing first!”

Not second or third, but first! And what is the most important thing to show about the outcome of an election? Who actually won.

In political systems like Germany’s, where we have no party getting anywhere near 50% of the vote, the usual one-bar-per-party bar charts totally fail to answer this most important question.

For example, in the following chart we can see the number of seats won by different political parties – but this does not tell us who won the election.
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