My makeover focused only on the most recent year, 2015. I did look at the trends, but didn’t find anything particularly of note to add to or support the story. I wanted to keep things simple. I played around with versions without maps, but this data set seems challenging to show in other formats. Any time I took the map out, I found myself wanting to know the detail of each mark: WHAT country is it and WHERE is it?
This week’s original chart came from CNBC.
Here’s what I like:
I happen to know this one was specifically designed for mobile. CNBC had a very specific brief that required the dashboard to render correctly on mobile devices. With that in mind, the dash is quite small and simple.
It has a nice “About” tooltip in the top left, where I’m likely to see it.
The lookup table does allow me to see individual numbers, should I wish to find them.
What I’d improve:
The colour palette seems arbitrary to me. I think there are nicer palettes available which could portray the categories more clearly
The circles in the lookup table make it hard to read the numbers.
I don’t like the vertically oriented labels in the upper table. There is space enough to have them horizontal
Here are a couple of ideas I rejected:
The timelines didn’t reveal too much and the dots left me feeling too curious about the actual country each dot represented. Hence I went back to a map.
I had a crazy idea that listing out all the countries next to a stacked bar would work. It didn’t. Or did it? Could you see that idea going anywhere?
This week my aim is to emphasize where the dataisn’t. I made the y-axis scale show the full range from 0% to 100% female representation. Why? In an ideal world, states should have approximately 50% female representation.
When I first explored the data, Tableau automatically tops out the y-axis just above the highest percentage state. This is reasonable but it means you don’t immediately see how low the representation is in some states. In the image below, you can see bunch of states are high on the y-axis. You have to stop yourself and wonder about truncating the UPPER end of the y-axis when showing percentages. Colorado and Vermont are the closest to 50% so well done them. But it’s still NOT 50% female representation.
I decided to ignore the values relating to gender split in the population: all the states are close enough to 50% to make any differences mere noise.
A map helps for quick lookup if the viewer is looking for a specific state
Things I would have changed:
Was this data best shown as a map? If the story is about underrepresentation, then show that, rather than just geographic information. Bar charts, or distributions could show that story more powerfully.
The colour scheme is, to me, a little muddy. High/low doesn’t pop out. The grey is especially unclear. Without looking at the key, would you be able to tell if grey was good, bad or middling? I wouldn’t know.
This week I decided to see what everyone else did before I did my own, to force me to come up with a new perspective.
Most people seem to have focussed on visualising the ages that these entrepreneurs made their millions/billions, reusing the same main fields as the original.
Since that’s what most others do, how could I show the same story in a different way?
I decided to focus on the other measures: the gap between millionaire and billionaire and their net worth. It’s not quite the same story, but I needed something original!
Did I succeed? I’m not sure.
As I write and reflect on this one, I don’t think my story is as compelling as the original. Not only that, I don’t think the scatterplot makes it clear! I do think the difference between Alan Sugar and Bill Gates is pretty clear, but as a scatterplot it’s just too hard to decipher for a story that’s better told in the manner of the original.
I do propose four changes to help focus on what I think the key story is: the growing obesity crisis.
Change the colour scheme. I found it a little hard to decode the colour scheme. Which colour is good and which is bad? I’ve changed to a red/grey scheme: now red is bad and pops out more clearly. My palette choice isn’t perfect as it’s not a truly continuous scale.
Change the order of the BMI categories. I’ve put the highest levels of BMI at the bottom, so it’s easier for us to see the growth of obesity.
Change the default view to be sorted showing the worst countries first.
What do you think? Would you have kept the original? What would you have changed? One thing I wonder about is whether the original colour scheme, harder as it was to decode, was more appealing because of its novelty and brightness? If you spent time looking at Ramon’s, did the colour scheme draw you in? Would my red/grey one have piqued your interest?
Tableau labels the zero on axes. There’s nothing wrong with that, unless you’re showing Ranks, when zero is meaningless. What I did here was to drop a text object over the top of the 0.
I also added the axis label (“Rank”) above the 1 as it’s more likely to be seen and read by a viewer at the top than halfway down the left hand side of the axis, oriented on its side.
How do you label both ends of a line of a slope chart? Simple: you just turn on labels and choose Start/End of line? Well, no, because then you end up with your labels misaligned:
One option is to just label one end of the line. This is ok, but sometimes reduces the speed to insight. If you want to label both ends, you need to duplicate your measure onto a dual axis, and set each label differently. One measure is set to label the start of the line, and the other is set to label the end of the line.
There is a downside to this: the marks are all duplicated. This can lead to the edges of the lines looking jagged.
This week reiterated that when you engage with data rather than just consume it, you learn more. This week’s data is fascinating. FiveThirtyEight’s original article is very good at explaining the perils of choosing just one measure to explain something as complicated as a city’s racial profile.
Understanding the nuances of statistical measures is vital at all times. This is especially true this year for people in the US and the UK. In the US, the election will be rife with statistical claims, and in the UK the EU Referendum is also awash with claim and counter-claim. Even when statistics being used are true, we need to be educated enough to judge if they are appropriate.
Here’s their great chart:
What do I like about their scatterplot?
Two trend lines. One is the 45 line, showing what a perfect correlation would be. The trend line for the data is in red. Having both helps you see more clearly how one measure skews the other.
It’s well labelled. Scatterplots do need time to digest as you work out what all the different areas of the chart mean. The annotations and title help greatly.
What would I improve?
I do find FiveThirtyEight’s grey background a tiny bit frustrating: I don’t know why they consciously reduce the contrast between foreground and background in this way. It’s only a small gripe.
The article discusses three measures but the chart only displays two of them.
I wanted my makeover to focus on comparing all three of the different measures in one view. I chose to highlight some of the cities they talk about in the article in order to see how the 3 measures change the rank of the city.
I imagined 3 rows, one for each measure. On the left of each row would be a description of the measure, and on the right, a tidy sorted bar chart. All bars would be grey except for the cities I wanted to highlight.
But it just didn’t quite work. The bars for the highlighted cities were too indistinct:
Since horizontal didn’t work (a shame, because horizontal charts fit better in a tweet!), maybe vertical would? Nope.
Note: writing up this post, I now think I could have succeeded in this design by dropping the 5 citites and using highlighting to show cities. Problem is, that would require people interacting with the viz to get anything out of it, and I’m trying to focus on static charts in my Makeovers.
The problem with both of these is the Integration/Segregation index. Because that crosses the zero line, many of the bars are tiny, so it’s hard to see individually highlighted bars.
My solution to this was to change the measure: instead of showing the actual value, I’d use Rank. Also, at the back of mind all along had been the words “Slope Chart”. I had initially wanted to avoid the slope because I seem to be using them for lots of my Makeovers.
Could I tell a slope story in a new way? Can slopes work horizontally?
I don’t think that really worked. Slopes seem like time lines in that we are totally accustomed to seeing them oriented horizontally. Is this is an aspect of our visual system or because convention has trained us to read them this way?
Of course, your data is also in the cloud. Our archipelago needs clouds, too, raining data from the devices to the local storage represented by the islands.
In the archipelago data flows around like water. Water is like the air around us. It contains the information. It flows freely. Polluted water corrupts your data and Pirates, literally, can land on your islands and steal your stuff.
Modern data architecture is an archipelago.
Or maybe it’s a Data Swimming Pool.
Your data’s the water. Data needs to be kept clean. Chlorine and Vacuums represent the ETL layer. Lifeguards are your disaster recovery processes. And kids who pee? Well that’s the SQL Injection attack, of course.
I need to move my eye many many times to decipher and compare things. Just comparing soft drinks between two age groups means I have to do a lot of cognitive work to find an answer.
The donut is arbitrary. A pie would have been better as there’s more of a mark to identify. If they REALLY wanted a donut, maybe they could have put the AMOUNT of added sugar as a label inside the donut.
The story is about soft drinks, primarily, but the colour for soft drinks doesn’t really stand out
The icons are ok, but what’s odd is how far away from the donuts they are. There’s too much white space between the donuts and the top of the icons.
Why is alcohol red? That makes it stand out the most, but the article is about soft drinks.
For my makeover, I decided to reduce the data to that most relevant to the story: how much of our added sugar comes from soft drinks. It shocks me to the core how much fizzy stuff our kids drink. 40% of added sugar coming from soft drinks? The BBC article also includes the actual volume of sugar, but we didn’t have that in our makeover dataset. It’s depressing.
Even if you kept all the food sources, the BBC’s approach could be improved by either a stacked bar, or even filling in the holes in the donuts. Notice that I’ve made Soft Drinks red in these examples.
Of all the three makeovers I’ve done this week, I think the pie might be my favourite: the part to whole relationship of the sugar really jumps out.
The interactivity lets you explore countries for yourself
The clear tick/cross icons on the right highlight where a country is equal or unequal in each category.
What could be better?
The circular layout serves no purpose. In fact, it confuses: it’s very hard to follow the categories around a circle to get to anything specific
The widths of the arcs are set by population, but that doesn’t have any bearing on women’s rights.
Colour is used arbitrarily.
The viz is not much more than a lookup table where you click to see details. The Guardian should have gone with a tabular layout at the least in order to make it easy for the viewer to find country/category.
I originally intended to build the same interactive viz as the Guardian, changing only to a tabular layout. As is often the way, though, I was semi-randomly exploring the data and the above view appeared. The data is detailed so it’s difficult to know how to show everything. In my makeover, I resorted to the tooltip.
With a rich dataset like this, it seems important to provide an interactive rather than static chart. But how do you serve the people who will interact and those who won’t?
My design is an attempt to please all. If you don’t interact, the layout is a sorted bar chart of the countries with the highest number of negative women’s right topics. Even without interactivity, a viewer sees a compelling story.
If you do interact, the tooltips give you context. It’s still imperfect because looking up a particular topic for all countries isn’t facilitated in my solution. Although Highlighting provides a partial solution.
I am happy with my use of highlighting to let you click on a category and scan through all the countries to see how they compare, at the same time as keeping all the data visible in the background.
I do need to note that the data is a challenge. I’d say my makeover is a proof of concept and you need to be aware of the following caveats.
Lots of countries have answers to only a few of the categories. 31 countries with missing answers are excluded.
For all bar one of the topics “No” represented inequality. But many countries had Null or N/A. Is that negative or not? For the purposes of this makeover I classed it as negative. It might be that they simply didn’t provide an answer.
Some data is confusing. For example, one category is about the constitution. The United Kingdom has a “no” against all the questions. Is that because it’s discriminatory? No. It’s because there isn’t a constitution. In my viz, it’s still classed as negative. I appreciate this is incorrect, but in the time constraints of MakeoverMonday I do not have time to clean the data fully.
Kristin Henry suggested a positive spin might be better. She’s right. My first update copied the headline and subhead direct from the Guardian. I took the lead from them, and we all know that news sites would rather tell you bad news than good. But this isn’t a bad news blog so here’s a postivie spin:
For my makeover I tried to achieve several things:
I tried to pose a different question. Instead of looking just at the CPI, I wanted to focus on whether the world is getting more or less corrupt.
On the face of it, the news looks ok: 83 countries got less corrupt between 2012 and 2015, whereas 63 got more corrupt. The story is of course more complicated than that. Looking at the world map, it looks like things are really bad in South America, but pretty good in Africa (lots of white means lots of countries becoming less corrupt).
However, let’s look at the dashboard for Africa:
Turns out the story isn’t as positive in Africa as initially thought:What’s going on?
More countries got more rather than less corrupt (25 versus 20). There’s lots of white in Africa because the countries that got less corrupt happened to be the bigger ones: they are more dominant in the filled map.
Look at the CPI slope charts. Although 20 countries became less corrupt, they already had very poor CPI scores. Sure, they became less corrupt, but that hasn’t made them safe havens for citizens. The picture’s improving, but not by much.
In terms of design, why did I make my choices?
Instead of red/green I chose red/white against a black background. I want to make something with had more punch. I think the end result is a little too much.
To show change, I chose to do slope charts. When you interact with the dashboard, each line is labelled and highlighted. Without the interactivity, I acknowledge it just looks like spaghetti.
I chose filled maps, but could have used dots on the map. I’m happy to agree with you if you felt dots would have been better.