Rising Tides, Rising Disasters?

One more day of Harvey-related content. At least I hope. (Who knows? Maybe someone will design a fantastic retrospective graphic?) Today, however, we look at a piece from the Economist about the rising number of weather-related disasters, but thankfully falling numbers of deaths. The piece has all the full suite of graphics: choropleths, line charts, and bar charts (oh my!). But I want to look at the bar chart.

A timeline of disaster causes around the world
A timeline of disaster causes around the world

I cannot tell from this chart whether there has been any change in the individual elements, the meteorological, hydrological, or climatological disasters. And unfortunately stacked bar charts do not let us see that kind of detail. They only really allow us to see total magnitude and the changes in the element at the bottom of the stack, i.e. aligned with the baseline. So I took their chart and drew the shapes as lines and realigned everything to get this.

My take
My take

You can begin to see that meteorological might be overtaking hydrological, but it is too early to tell. And that right now, climatological causes are still far behind the other two.

Credit for the piece goes to the Economist Data Team.

Credit for mine goes to me.

Harvey’s Rainfall Part Two

Let’s consider today a follow-up to yesterday’s piece. (No, I do not believe I have ever done a follow-up piece, but why not start now all these years later.)

Yesterday we looked at the Post, Journal, and Times for their coverage of the fallen rain amounts in southeast Texas. But at the time, we only had actual totals from the Post and Journal. The Times had only produced a projection map. The Times piece yesterday was perhaps the most underwhelming of the three, though it certainly did some things correctly, namely it was small, simple, and quick to get the reader to the point that Houston was likely to be flooded by storm’s end.

Well that had changed by the time I got home last night.

The Times' graphic
The Times’ graphic

What is different about this piece? Well this one is an animated .gif showing the cumulative rainfall. In other words, Texas starts dry and every hour just makes the map bluer and bluer. An additional feature that I find particularly useful is the dot map, which indicates where the heaviest rain was falling in each hour. Especially early on in the event, you can see the bands of rain sweeping in from the Gulf.

The bins also work better here, though I wonder if more segregation or a different palette would have worked a bit better. But, my biggest critique is the same I have with many animated .gifs: the looping. And unfortunately I do not have an easy solution. You certainly need to see it loop through more than once to understand the totality of the rainfall. But then I really do want to be able to examine the final map, or at least final as of 03.00 today.

Anyway, this was a really nice piece that should have been showcased alongside the others yesterday.

Credit for the piece goes to Gregor Aisch, Sarah Almukhtar, Jerey Ashkenas, Matthew Bloch, Joe Burgess, Audrey Carlsen, Ford Fessenden, Troy Griggs, K.K. Rebecca Lai, Jasmine C. Lee, Jugal K. Patel, Adam Pearce, Bedel Saget, Anjali Singhvi, Joe Ward, and Josh Williams.

Harvey’s Rainfall Totals

Hurricane Harvey landed north of Corpus Christi, Texas late Friday night. By Monday morning, Houston has been flooded as nearly two feet of water have fallen upon the city, built on and around wetlands long ago paved over with concrete. Naturally the news has covered this story in depth all weekend. Even leading up to it, when I was still posting eclipse things, various outlets had projections and why we should care graphics. But as the storm begins to move back into the Gulf—only to move back inland tomorrow—I wanted to compare some of the graphics I have been seeing.

Of course, not all graphics are the same, let alone cover the same things. So this morning we are looking at just the rainfall total maps of a few different outlets.

From the Washington Post, we have the following graphic.

The Post's rainfall graphic
The Post’s rainfall graphic

The palette chosen performs well at quickly scaling up to the record level of rainfall, i.e. the 20+ inches realm, but quickly shifting from the green–blue palette into dark purples.

Then we have the Wall Street Journal’s graphic.

The Journal's graphic
The Journal’s graphic

Here we have a more familiar blue–red diverging spectrum. The point of divergence set to 20 inches.

Lastly, we have the New York Times graphic. Though in this case, it’s not an exact like-for-like comparison. I could not find a graphic mapping total rainfall, instead this is for projected rainfall totals. But the design is for the same type of map, i.e. how much rain falls in a location.

The Times' graphic
The Times’ graphic

The Post takes the closest approach to the true continuous spectrum palette, where the shift from dry to drenched is gradual. It makes for a smoother, more blended looking map. Somewhere around that 20 inch point, however, the palette shifts from the green to blue range to purple. It emphasises the record-hitting point, but otherwise the totals are presented as more fluid. Perhaps correctly since rain does not neatly fall evenly into pixels.

By comparison, the Journal segments the rainfall totals into bins of blues. The scale is not even, the lighter blues incorporate two inches, the darkers upwards of five. And then again, like the Post, separate 20+ as a different colour, here switching to reds.

Lastly the Times keeps to a simple segmented bin palette of all blues. 20+ inches is rendered is just a dark blue.

Each map has pluses, each has minuses. The Times map, for example, is simple and quick to understand. Southeastern Texas will be wet by the middle of next week. If your goal is only to communicate that point, well this map has done its job. It is worth noting, again, that this is a map of projections. Because the other thing missing from this map is the storm’s path. So if the goal were to showcase the rainfall along the storm’s path, well this graphic does not accomplish that nearly as well as the other two.

The Post and the Journal both show the track of the storm. The Journal takes it one step further and plots its projected course through Thursday. This helps us really see if not understand the east side problem of hurricanes. That is, the eastern quadrants of hurricanes typically experience the heaviest amounts of rain. And as the darker portions of the map all fall to the north and east of those lines, it reaffirms this for us.

So what really differentiates the two? The colour palette and its application. The Post’s palette is more natural as, again, rain does not fall neatly into bins and instead makes for blurred and messy totals across a map. Separating the heaviest rains into the purples, however, makes a lot of sense as that amount of rainfall, as we are seeing this morning, makes for a mess in Houston.

But the point of a graphic is to translate nature and the observed into a digestible and pointed statement of the observed. What should I learn? Why should I care? The Journal, like the Post, does a fantastic job of splitting out the 20+ inch totals by using a divergent palette. But instead of blending into that colour, the distinction is sharp. And then below that threshold, we get rainfall totals segmented into just a few bins. These help the reader see, also more starkly because of the selection of the specific blues, just where the bands of heavy rain will fall.

I do want to point out, however, that all of these maps occur in articles with lots of other fantastic graphics that visually explore lots of details about the story. And in particular, I want to highlight that the normal bit where I state the credits includes a lot of people. Creating a whole host of graphics to support a story takes a lot of work.

Credit for the Washington Post piece goes to Darla Cameron, Samuel Granados, Chris Alcantara, and Gabriel Florit.

Credit for the Wall Street Journal piece goes to Bradley Olson, Arian Campo-Flores, Miguel Bustillo, Dan Frosch, Erin Ailworth, Christopher M. Matthews, and Russell Gold.

Credit for the New York Times piece goes to Gregor Aisch, Sarah Almukhtar, Jeremy Ashkenas, Matthew Bloch, Joe Burgess, Audrey Carlsen, Ford Fessenden, Troy Griggs, K.K. Rebecca Lai, Jasmine C. Lee, Jugal K. Patel, Adam Pearce, Bedel Saget, Anjali Singhvi, Joe Ward, and Josh Williams.

Alaskan (im)Permafrost

I woke up this morning and before breakfast I opened the door to bring in today’s edition of the New York Times. I enjoy reading the paper, or at least a few articles, over breakfast (and more often than not preparing a post for here at Coffeespoons.me). Some of the best days are when I open the door and find a giant piece of data visualisation there above the fold. Other images, for example the other day’s eclipse coverage, also strike me, but as someone who visualises data as part of his career, I particularly enjoy things like maps. (I should point out I also do editorial design, so things like this layout are even closer to the intersection of my interests.)

Lo and behold, this morning I opened the door and we had the shrinking permafrost of Alaska this morning.

Now that is basically it. I have a crop of the map at the end here, but the map was the extent of the data visualisation in the article. Indeed, other articles in today’s edition carried more interesting graphics—I took photos to hopefully circle back—but the nerd I am, I really do get a kick finding a paper like this in the morning.

The graphic itself occupies half the space above the fold and the bright cyan and magenta steal the user’s attention. Even the headlines of the other articles recede behind the Alaska maps.

White space around the maps subtly helps focus attention on the piece. To be fair, the shape of Alaska with its archipelagos and bays along with the southeast extension help to create that space. A more squarish shape, say Colorado, would not quite have the same effect.

If I had to critique anything, I might have placed the city labels, especially Fairbanks, and the state label elsewhere to enhance their legibility. But at that point, I’m really just quibbling around the edges.

Red means it's warming up
Red means it’s warming up

Credit for the piece goes to Jeremy White.

Labour Marches On (into Tory Housing?)

We have a nice little piece from the Economist today, a look at the electoral majority for London-area constituencies and how their housing prices may begin to draw out priced-out Labour votes from London proper.

The political impact of scarce housing supply
The political impact of scarce housing supply

What I really like from the design side is the flip of the traditional choropleth density. In other words, we normally see the dark, rich colours representing high percentages. But here, those high majority constituencies are not the ones of focus, so they get the lighest of colours. Instead, the designers point attention to those slimmest of majorities and then offer the context of average home prices.

Credit for the piece goes to the Economist’s Data Team.

Home Vacancies in Kensington and Chelsea

I added Chelsea to make doubly certain for my Philadelphia audience that you did not think I was referring to Philly’s Kensington. Why? Because today’s piece comes from the Guardian and refers to the neighbourhood where the Grenfell Tower caught fire and the inferno killed dozens of people.

A north–south divide
A north–south divide

This is not the most complex piece, but I really like the annotations and notes on the choropleth. They add a great amount of detail and context to a graphic that I imagine many places would be okay leave as is. I can see why the colour palette differs for the two maps, but I wonder if it could have been made to work as a unified palette.

Credit for the piece goes to the Guardian graphics department.

Bus Transit in Philadelphia

I have lived in Philadelphia for almost ten months now and that time can be split into two different residences. For the first, I took the El to and from Centre City. For the second, I walk to and from work. I look for living spaces near transit lines. In Chicago I took the El for eight years to get home. But to get to work, I often used the 143 express bus. Personally, I prefer trains and subways to busses—faster, dedicated right-of-way, Amtrak even has WiFi. But, busses are an integral part of a dense city’s transit network. You can cram dozens of people into one vehicle and remove several cars from the road. Here in Philadelphia, however, as the Inquirer reports, bus ridership is down over the last two years at the same time as ride-hailing apps are growing in usage.

For those interested in urban planning and transit, the article is well worth the read. But let’s look at one of the graphics for the article.

Lots of red in Centre City
Lots of red in Centre City

The map uses narrow lines for bus routes and the designer wisely chose to alternate between only two shades of a colour: high and low values of either growth (green) or decline (red). But, and this is where it might be tricky given the map, I would probably dropdown all the greys in the map to be more of an even colour. And I would ditch the heavy black lines representing borders. They draw more attention and grab the eye first, well before the movement to the green and red lines.

And the piece did a good job with the Uber time wait map comparison as well. It uses the same colour pattern and map, small multiple style, and then you can see quite clearly the loss of the entire dark purple data bin. It is a simple, but very effective graphic. My favourite kind.

Still haven't used Uber yet. Unless you count the times I'm being put into one by a friend…
Still haven’t used Uber yet. Unless you count the times I’m being put into one by a friend…

Anyway, from the data side, I would be really curious to see the breakout for trolleys versus busses—yes, folks, Philly still has several trolley lines. If only because, by looking at the map, those routes seem to be in the green and growing category. So as I complain to everyone here in Philly, Philly, build more subways (and trolleys). But, as the article shows, don’t forget about the bus network either.

Credit for the piece goes to the Inquirer graphics department.

The Donald and The Donald Subreddit

I don’t use Reddit. But things begin to made sense for me in this article from the Economist as it explained the origins behind Trump’s weird tweet of himself beating up a CNN-headed wrestler.

Unfortunately I don't understand how Reddit works well enough to make full sense of these
Unfortunately I don’t understand how Reddit works well enough to make full sense of these

I think the thing perhaps lacking from the graphic is a line that tracks Trump’s approval or popularity. The article mentions that explicitly and it would be interesting to see that track over time. Although I certainly understand how stacking so many line charts above each other could become difficult to compare.

And my final critique are the Election Day outliers. They are above the y-axis maximum. But I wonder if there couldn’t have been a way of handling the outlier-ness of the datapoints while remaining true to the chart scales.

Credit for the piece goes to the Economist graphics department.

Traffic Accidents in Philadelphia

I’m working on a set of stories and in the course of that research I came across this article from Philly.com exploring traffic accident in Philadelphia.

Lots of red there…
Lots of red there…

The big draw for the piece is the heat map for Philadelphia. Of course at this scale the map is pretty much meaningless. Consequently you need to zoom in for any significant insights. This view is of the downtown part of the city and the western neighbourhoods.

A more granular view
A more granular view

 

As you can see there are obvious stretches of red. As a new resident of the city, I can tell you that you can connect the dots along a few key routes: I-76, I-676, and I-95. That and a few arterial streets.

Now while I do not love the colour palette, the form of the visualisation works. The same cannot be said for other parts of the piece. Yes, there are too many factettes. But…pie charts.

 

This is the bad kind of pie
This is the bad kind of pie

From a design standpoint, first is the layout. The legend needs to be closer to the actual chart. Two, well, we all know my dislike of pie charts, in particular those with lots of data points, which this piece has. But that gets me to point three. Note that there are so many pieces the pie chart loops round its palette and begins recycling colours. Automotives and unicycles are the same blue. Yep, unicycles. (Also bi- and tricycles, but c’mon, I just want to picture some an accident with a unicycle.)

If you are going to have so many data points in the pie chart, they should be encoded in different colours. Of course, with so many data points, it would be difficult to find so many distinguishable but also not garish colours. But when you get to that point, you might also be at the point where a pie chart is a bad form for the visualisation. If I had the time this morning I would create a quick bar chart to show how it would perform better, but I do not. Trust me, though, it would.

Credit for the piece goes to Michele Tranquilli.

The Disappearing Urban Middle Class

Today we look at income in American cities and in particular the middle class disappearance. The Guardian published the graphics, but they originate with Metrocosm, LTDB at Brown, and IPUMS National Historical Geographic Information System. So what are we looking at? Well, the big one is a set of small multiples of cities and their income breakdowns as percentages of city census tracts. This screenshot is static, but the original is an animated .gif.

The flattening of the curve
The flattening of the curve

I have a few issues with the design of the graphic, the most important of which is the colour palette. If the goal is to focus on the decline of the middle class—and I admit that may be the point of the Guardian’s authors and not the original authors—why are the most visually striking colours at the top of the income distribution. Instead, you would want to draw attention to the middle of each chart, not the right. And if the idea was that the darker colours represent the higher income groups, well the positioning of each bar on the chart and the axis labelling does that already. After all, if anything, the story is that in a number of cities the middle class has shrunk while the lower income groups have grown. And you can barely see that with the lower income groups coloured yellow.

My other issues are more minor design things such as the city labelling. I kept reading the label as being below the bars, not above as it actually is.

And then I wonder if a different chart form would be more effective at showing the decline in the middle class. Perhaps a line chart plotting the beginning and end points for each cohort?

Then the piece gets into some three-dimensional maps that you can spin and rotate.

Just stop
Just stop

Yeah. Shall I count the ways? A more conventional choropleth would have served the purpose far more effectively. The dimensionality hides lower income tracts behind higher ones. The solution? Allow the user to rotate and spin the map? No, get rid of the dimensionality. It offers little to the understanding of the underlying data. Not to mention, are the areas of shadows shadows? Or are they another bin or cohort of income?

And then you have to read the piece to get a fuller understanding of my criticism.

But don’t worry, I can quote it.

Chicago was largely successful transitioning away from manufacturing to a service-based economy. This shift is evident in the bifurcated pattern present in 2015 – a heavy concentration of wealth in the business/financial district and marked decline in the surrounding area.

Those of you who read this blog from Chicago or who have lived in Chicago will pick up on it. The rest of you not so much. The concentration of wealth is not located in the business/financial district. Those dark red skyscrapers are not actual skyscrapers, they are census tracts located not in the financial district, but the areas of River North, Old Town, Gold Coast, &c. Thinking of the issue more logically, yes incomes are up in cities that are doing well. But how many of those very wealthy live on the same block as their office? Not many. Your higher income is going to be concentrated in residential or mixed-residential neighbourhoods near, but not in the business/financial district.

The data behind this work fascinates me. I just wish the final graphics had been designed with a bit more consideration for the data and the stories therein. And a little bit of proper understanding of the cities and their geography would help the text.

Credit for the piece goes to Metrocosm, LTDB at Brown University, and IPUMS National Historical Geographic Information System.