There is nothing super sophisticated in these charts, but I love them all the same. The Economist Intelligence Unit (EIU) published its rankings of the world’s most liveable cities and this year Vienna knocked off Melbourne for top spot. But what about the rest of the list?
Thankfully the Economist, a related company, put together a graphic highlighting important or noteworthy cities among the entire dataset. It is a wonderful tangle of light grey lines that have select cities highlighted in thicker strokes and brighter colours. Labelling each city would be too tricky at this scale.
I’m okay with the occasional rainbow spaghettiThat said about labelling each city, a few years back I worked on a similar top cities in a category datagraphic for Euromonitor International. We took a similar approach and coloured lines by region, but we presented the entire dataset and then complemented it by some additional charts to the side.
What is really nice about the Economist piece, however, is that they opted not to show the whole dataset. This could be a business decision, if people want to find where a particular city they could be persuaded to either outright subscribe or otherwise provide contact information in exchange for access to the data. Either way, the result is a piece that has space to provide textual context about why cities rose or fell over the years.
I think I like these types of pieces because there is so much to glean from getting lost in the chart. And this one from the Economist does not disappoint.
Credit for the liveability piece goes to the Economist Data Team.
Everyone is probably familiar with Venice, slowly sinking below the Adriatic. But, did you know the city of Jakarta, Indonesia is also sinking?
The BBC published an informative article about the city’s looming problem and the piece includes several nice graphics. The screenshot below is an interactive timeline of the amount of subsidence, or sinking, in the the Jakarta region. It’s been notably worst along the coast. But the striking part are the forecasts for 2025 and 2050 that place the city in danger.
Photography of the scale of the subsidence feature throughout the story. And about halfway through is a nice motion graphic piece that attempts to explain the sinking. I am not certain it is the best graphic, after all it references two US NBA stars and I wonder how well known they are. (Whereas everyone clearly knows who David Ortiz is.)
I was aware of Jakarta’s peril, but until reading this article, I had not realised just how imperiled the city really is.
Credit for the piece goes to the BBC graphics department.
Today is Tuesday, 14 August. We are now 12 weeks away from the 2018 midterms. That is just three months away. Coverage will only intensify in the weeks to come, and you can be certain that if there are pieces worth noting, I will do that. But to mark the date I went with this choropleth map from the New York Times.
Nothing too crazy here. Likelihood of results colour the districts. The darker the blue, the more solid the Democratic seat. The darker the red, the more solid the Republican one. But what this map does really well is it excludes the likely’s and the solids and sets them to a light, neutral grey. You can still hover over a district if you are curious about where it falls, but, in general those have been excluded from the consideration set because they are not the districts of the most national attention.
Secondly, note the state labels. States like Wyoming that have no competitive seats have no label. After all, why are we labelling things that have no impact on this story, again, the competitive races. Fewer labels means fewer distracting elements in the graphic.
Finally, the piece includes the ability to zoom into a region. After all, for those of us living in urban areas, our districts are geographically tiny compared to the at-large or state-wide seats like in Wyoming, the Dakotas, and Alaska. Otherwise, good luck trying to find the Illinois 5th or Pennsylvania 3rd.
We have been looking at tariffs a little bit this week, but unfortunately one of the side effects of tariffs is job losses. And of course when it comes to people losing jobs, not all countries in the developed world handle them the same. Last month the Washington Post published an article examining how those countries compare in a number of related metrics such as unemployment compensation, notice for termination, and income inequality.
It uses a series of bar charts to show the dataset and reveal how the United States fares poorly compared to its peers. The chart above looks at the earning needed for termination from employment and the differences are stark. The outlined bar chart shows longer tenured employees and the full bars as coloured. Of course this makes it look like a stacked bar chart or filled bar chart. Instead I wonder if a dot plot would be clearer. It would eliminate the confusion in determining what if any share of the empty bar is held by the full bar.
The chart for unemployment insurance versus assistance is a bit better. Here the bar represents insurance and the lines assistance. I like how the lines continue off beyond the margins to indicate an unlimited timeframe for assistance. However, for those countries where assistance is short-lived, the bars versus lines again begin to look like an instance of a share of a total, which they are not.
I am a millennial. That broadly means I am destroying and/or ruining everything. It also means I am obsessed with things like avocado toast. It also means I am not buying a house. Thankfully the Economist is on top of my next fad: indoor houseplants.
Your author will admit to having a few: a hanging plant, an Easter lily, an aloe plant and its children, and a dwarf conifer. Just don’t ask me how they’re doing. (Hint: not well.) Turns out I am not a plant person.
In terms of the graphic, though, what we have is a straight up set of small multiples of line charts. The seasonality mentioned in the article text appears quite clearly in a number of plants.
But is Swiss Cheese really a plant?
Credit for the piece goes to the Economist Data Team.
Last Tuesday we looked at a print piece from the New York Times detailing the share price plunge of Facebook after the company revealed how recent scandals and negative news impacted its financials. Well, today we have a piece from last week that shows how large Apple is after it hit a market capitalisation of one trillion US dollars.
The piece itself is not big on the data visualisation, but it functions much like the Facebook piece, as a blend of editorial design and data visualisation. The graphic falls entirely above the fold and combines a factette and maybe we could classify it as a deconstructed tree map. It uses squares where, presumably, the area equates to the company’s value. And the sum total of those squares equals that of one trillion dollars, or the value of Apple.
In terms of design it does it well. The factette is large enough to just about stretch across the width of the page and so matches the graphic below it in its array of colours. Why the colours? I believe these are purely aesthetic. After all, it is unclear to me just what Ford, Hasbro, and General Mills all have in common. In a more straight data visualisation piece, we might see colour used to classify companies by industry, by growth in share price or market share. Here, however, colour functions in the editorial space to grab the reader’s attention.
The design also makes use of white space surrounding the text, much like the Facebook piece last week, to quiet the overall space above the fold and focus the reader’s attention on the story. Note that the usual layout of stories on the page continues, but only after the fold.
When we keep in mind the function of the piece, i.e. it is not a straight-up-explore-the-data type of piece, we can appreciate how well it functions. All in all this was a really nice treat last Friday morning.
Credit for the piece goes to Karl Russell and Jon Huang.
We are now less than 100 days away—95 to be exact—from the 2018 midterm elections here in the United States. As we get closer and closer we not only get more information from polls, but also campaign finance reports. Those can sometimes serve as a proxy for support as lots of grassroots support can dump lots of cash in a candidate’s war chest. Wheras a candidate who drums up little support might find him or herself with scant funds to fight the campaign.
So what does that funding tell us right now? Well last week Politico posted an article looking at that data. They broke the dataset into chunks by the likelihood of the results. This screenshot is of Pennsylvania’s 1st Congressional District.
Each district is represented by a dot plot, with the total money raised by each candidate plotted, the distance in grey being the amount by which the Democrat outraised the Republican.
This is a nice piece as the hover state provides a nice grey bar behind the district to focus the user’s attention. Then for the secondary level of information in terms of cash on hand for the Democrats, i.e. who has cash now, we get the dot filled in versus the open state for simply money raised. Then of course the hover state reveals the actual numbers for the two candidates along with the difference between the two.
The funny thing with this particular district, the Pennsylvania 1st, is that Wallace is not necessarily raising a lot of money. He is a self-funding millionaire. He also is not the most electable Democrat in a competitive seat. It will be fascinating to watch how this particular district performs over the next few months, but most importantly in November.
I really like what the designers did here. First and foremost the key chart is a ranking chart showing the popularity of languages since 1988—Java and C have consistently been at the top. But other languages no longer relevant are not even shown. (Where are you, Actionscript?) Those that are both relevant and also mentioned are colour coded within the set.
But the truly nice thing is being able to use the empty space of the lower-left area of the chart to add some context. It shows the growth in Google searches since 2010 in searches for Python.
Bonus note, look at that rise in R since 2008.
Credit for the piece goes to the Economist Data Team.
Last Thursday, Facebook’s share price plunged on the news of some not so great numbers from the company on its quarterly earnings report. The data and number itself is not terribly surprising—it is a line chart. But what I loved is how the New York Times handled this on the front of the Business section on Friday morning.
I found the layout of the page and that article striking. In particular, each day of the share price is almost self-contained in that the axis lines start and stop for each day. I question the thickness of the stroke as something a little thinner might have been a bit clearer on the data. However, it might also have not been strong enough to carry the attention at the top of the page. As it is, that attention is needed to draw the reader down the page and then down across the fold.
Additionally, the designers were sensitive to the need to draw that attention down the page. In order to do that they kept the white space around the graphic and kept the text to two small blocks before moving on to the interior of the section.
Credit for the story goes to Matthew Philips. Although I’m pretty sure the page layout goes to somebody else.
The weather in Philly the past week has been just gross. It reminds of Florida in that it has been hot, steamy, storms and downpours pop up out of nowhere then disappear, and just, generally, gross. I do not understand how people live in Florida year round. Anyway, that got me thinking about this piece from a month ago in the New York Times. It looked at the impact of climate change and living conditions in South Asia. Why is South Asia important? Well, it is home to nearly a billion people, a large number of whom are poor and demanding resources, and oh yeah, has a few countries that have fought several wars against each other and are armed with nuclear weapons. South Asia is important.
The map from the piece—it also features a nice set of small multiples of rising temperatures in six countries—shows starkly how moderate emissions and the high projection of emissions will impact the region. Spoiler: not well. It notes how cities like Karachi, for example, will be impacted as hotter temperatures mean lower labour productivity means worse public health means lower standard of living. And it doesn’t take a rocket scientist to see how things like demand for water in desert or arid areas could spark a conflict between Pakistan and India. Although, to be very clear, the article does not go there.
As to the design of the graphic, I wonder about the use of white for no impact and grey for no data. Should they have been reversed? As it is, the use of white for no impact makes the regions of impact, most notably central India, stand out all the more clearly. But it then also highlights the regions of no data.
Credit for the piece goes to Somini Sengupta and Nadja Popovich.