Urban Heat Islands

Yesterday was the first day of 32º+C (90º+F) in Philadelphia in October in 78 years. Gross. But it made me remember this piece last month from NPR that looked at the correlation between extreme urban heat islands and areas of urban poverty. In addition to the narrative—well worth the read—the piece makes use of choropleths for various US cities to explore said relationship.

My neighbourhood's not bad, but thankfully I live next to a park.
My neighbourhood’s not bad, but thankfully I live next to a park.

As graphics go, these are effective. I don’t love the pure gradient from minimum to maximum, however, my bigger point is about the use of the choropleth compared to perhaps a scatter plot. In these graphics that are trying to show a correlation between impoverished districts and extreme heat, I wonder if a more technical scatterplot showing correlation would be effective.

Another approach could be to map the actual strength of the correlation. What if the designers had created a metric or value to capture the average relationship between income and heat. In that case, each neighbourhood could be mapped as how far above or below that value they are. Because here, the user is forced to mentally transpose the one map atop the other, which is not easy.

For those of you from Chicago, that city is rated as weak or no correlation to the moderately correlated Philadelphia.

I lived near the lake for eight years, and that does a great deal for mitigating temperature extremes.
I lived near the lake for eight years, and that does a great deal for mitigating temperature extremes.

Granted, that kind of scatterplot probably requires more explanation, and the user cannot quickly find their local neighbourhood, but the graphics could show the correlation more clearly that way.

Finally, it goes almost without saying that I do not love the red/green colour palette. I would have preferred a more colour-blind friendly red/blue or green/purple. Ultimately though, a clearer top label would obviate the need for any colour differentiation at all. The same colour could be used for each metric since they never directly interact.

Overall this is a strong piece and speaks to an important topic. But the graphics could be a wee bit more effective with just a few tweaks.

Credit for the piece goes to Meg Anderson and Sean McMinn.

Baby You Can Drive My Non-automobile Personal Mode of Transportation

Last week was the climate summit in New York, and the science continues to get worse. Any real substantive progress in fighting climate change will require sacrifices and changes to the way our societies function and are organised, including spatially. Because one area that needs to be addressed is the use of personal automobiles that consume oil and emit, among other things, carbon dioxide. But living without cars is not easy in a society largely designed where they are a necessity.

But over at CityLab, Richard Florida and Charlotta Mellander created an index trying to capture the ability to live without a car. The overall piece is worth a read, but as usual I want to focus on the graphic.

The Northeast is where it's at with its dense cities designed for a pre-automobile era
The Northeast is where it’s at with its dense cities designed for a pre-automobile era

It’s nothing crazy, but it really does shine as a good example of when to use a map. First, I enjoy seeing metro maps of the United States used as choropleths, which is why I’ve made them as part of job at the Philly Fed. CityLab’s map does a good job showing there is a geographic pattern to the location of cities best situated for those trying to live a car-free life. Perhaps not surprisingly, one of the big clusters is the Northeast Corridor, including Philadelphia, which ranks as the 17th best (out of 398) and the 7th best of large metro areas (defined as more than one million people), beating out Chicago, ranked 23rd and 8th, respectively.

Design wise I have two small issues. First, I might quibble with the colour scheme. I’m not sure there is enough differentiation between the pink and light orange. A very light orange could have perhaps been a better choice. Though I am sympathetic to the need to keep that lowest bin separate from the grey.

Secondly, with the legend, because the index is a construct, I might have included some secondary labelling to help the reader understand what the numbers mean. Perhaps an arrow and some text saying something like “Easier car-free living”. Once you have read the text, it makes sense. However, viewing the graphic in isolation might not be as clear as it could be with that labelling.

Credit for the piece goes to David Montgomery.

The Map

I mean come on, guys, did you really expect me to not touch this one?

Well we made it to Friday, and naturally in the not so serious we have to cover the sharpie map. Because, if the data does not agree with your opinions, clearly the correct response is to just make shit up.

By now you have probably all heard the story about how President Trump tweeted an incorrect forecast about the path of Hurricane Dorian, warning how Alabama could be “hit (much) harder than anticipated”. Except that the forecast at the time was that Alabama wasn’t going to be hit. Cue this map, days later. As in days. As in this news story continued for days.

Note the sharpie weirdly extending the cone (in black, not the usual white) into Florida and onward into Alabama.
Note the sharpie weirdly extending the cone (in black, not the usual white) into Florida and onward into Alabama.

So to be fair, I went to the NOAA website and pulled down from their archive the cone maps from the date of the graphic Trump edited, and the one from the day when he tweeted about Alabama being hit by the hurricane.

Important to note that this forecast dates from 29 August. This press conference was on 4 September. He tweeted on 1 September. So in other words, two days after he used the wrong forecast, he had printed a big version of a contemporaneously two-day old forecast to show that if he drew a sharpie line on it, it would be correct.

Here is the original, from the National Hurricane Centre, for 29 August. Note, no Alabama.

No Alabama in this forecast, the OG, if you will (and if I'm using that term correctly).
No Alabama in this forecast, the OG, if you will (and if I’m using that term correctly).

And then Trump tweeted on 1 September. So let’s take the 02.00 Eastern time 1 September forecast from NOAA.

By 30 August the forecast was already tracking northward, not westward. So by 1 September the idea that the hurricane would hit Alabama was just nonsense.
By 30 August the forecast was already tracking northward, not westward. So by 1 September the idea that the hurricane would hit Alabama was just nonsense.

Definitely no Alabama in that forecast.

This could have all gone away if he had just admitted he looked at the wrong forecast and tweeted an incorrect warning. Instead, we had the White House pressuring NOAA to “fix” their tweet.

Now we can all chalk  this up as funny. But it does have some serious consequences. Instead of people in the actual path of Dorian preparing, because of the falsely wide range of impacts the president suggested, people in Alabama needlessly prepared for a nonevent.

But more widely, as someone who works with data on a daily basis, we need to agree that data is real. We cannot simply change the data because it does not fit the story we want to tell. If I could take a screenshot of every forecast and string them together in an animated clip, you would see there was never any forecast like the sharpie forecast. We cannot simply create our own realities and choose to live within them, because that means we have no common basis on which to disagree policy decisions that will have real world impacts.

Credit for the photo goes to Evan Vucci of the AP.

Credit for the National Hurricane Centre maps goes to its graphic team.

Merging of the States

Dorian now speeds away from Newfoundland and into the North Atlantic. We looked at its historic intensity last week. But during that week, with all the talk of maps and Alabama, I noted to myself a map from the BBC that showed the forecast path.

Did New Jersey eat Delaware?
Did New Jersey eat Delaware?

But note the state borders. New Jersey and Delaware have merged. Is it Delawarsey? And what about Maryland, Virginia, and the District of Columbia? Compare that to this map from the Guardian.

Here the states are intact
Here the states are intact

What we have are intact states. But, and it might be difficult to see at this scale, the problem may be that it appears the BBC map is using sea borders. I wonder if the Delaware Bay, which isn’t a land border, is a reason for the lack of a boundary between the two states. Similarly, is the Potomac River and its estuary the reason for a lack of a border between Virginia, Maryland, and DC?

I appreciate that land shape boundary files are easy, but they sometimes can mislead users as to actual land borders.

Credit for these pieces go the BBC graphics department and the Guardian graphics department.

Greenland Is Melting

There is a lot going on in the world—here’s looking at you Brexit vote today—but I did not want to miss this frightening article from the BBC on the melting of Greenland’s ice. It’s happening. And it’s happening faster than thought.

There are several insightful graphics, including the standard photo slider of before and after, a line chart showing the forecast rise of sea levels within the possible range. But this one caught my eye.

Alarming rates along the coast.
Alarming rates along the coast.

The colour palette here works fairly well. The darkest reds are not matched by a dark blue, but that is because the ice gain does not match the ice loss. Usually we might see a dark blue just to pair with a dark red, but again, we don’t because the designers recognised that, as another chart shows, the ice loss is outweighing the gains, though there are some to be found most notably at the centre of the ice sheets. This is a small detail, but something that struck me as impressive.

My only nitpick is that the legend does not quantify the amounts of gain or loss. That could show the extremes and reinforce the point that the loss is dwarfing the gain.

Credit for the piece goes to the BBC graphics department.

Hong Kong Identity

One of the things I have been following closely the last few months has been the protests in Hong Kong. The city is one of China’s few Special Administrative Regions—basically the former British colony of Hong Kong and the former Portuguese colony of Macau, two cities bordering mainland China and separated by the Pearl River estuary.

Long story short, but since 1997 Hong Kong should enjoy 50 years of a legal system that is more aligned to that of its former status of a British colony than that of mainland China. But increasingly since Xi Jinping took power, he has been eroding those rights and the youth of Hong Kong have taken to the streets to protest, a right they enjoy but not the rest of mainland Chinese.

And so we have a survey looking at the identity by which those people living in Hong Kong choose to identify.

And it’s not Chinese.

Not a good trend for Beijing
Not a good trend for Beijing

From a news perspective, this poses problems for a Beijing-based Chinese government that is making pains to promote a greater Chinese identity throughout the world, least of all by pushing for a reunification with Taiwan by force if necessary. A generation of several million Hong Kongers and the way they raise their children, in addition to their friends and supporters abroad, weakens the authority of Beijing.

Hence the threat of a Tiananmen Square style crackdown on Hong Konger protestors.

Alas, the United States has been far more concerned with its trade dispute than it has been the democratic and human rights of several million people. At least, that is the impression given by the White House.

But, as to the design, I do not love the spaghettification of the line charts. Though I do appreciate that the Hong Kong identity has been separated by the maroon-coloured line. I wonder if labelling the lines in the small multiples is necessary given the decision to include the legend at the top of the chart.

The other tricky thing with this type of chart is that the data series is a population cohort. And yet the data is based on a time series. And so the cohorts vary over time. It might not be entirely clear to the audience that this (appears to be)/is a sample of people of an age at a particular date. How do those people change over the years? It’s hard to see that trend by separating out the data.

Overall, it’s a solid piece. And it’s important given the gravity of the protests in Hong Kong.

Credit for the piece goes to the Economist Data team.

A Very Loud Tube

As all my readers probably know, I love London. And in loving London, I love the Tube and the Oyster Card and all that goes along with Transport for London. But, I have noticed that sometimes when I take the Underground, there are segments where it gets a bit loud, especially with the windows open. The Economist covered this in a recent article where they looked at some data from a London-based design firm that makes noise protective gear. (For purposes of bias, that seems important to mention here.)

The data looks at decibels in a few Underground lines and when the levels reach potentially harmful levels. I took a screenshot of the Bakerloo line, with which I am familiar. (At least from Paddington to Lambeth.) Not surprisingly, there are a few segments that are quite loud.

I definitely recall it being loud
I definitely recall it being loud

I like this graphic—but like I said about bias, I’m biased. The graphic does a good job of using the above the 85-decibel line area fill to show the regions where it gets loud. And in general it works. However, if you look at the beginning of the Bakerloo line noise levels the jumps up in down in noise levels, because they happen so quickly in succession, begin to appear as a solid fill. It masks the importance of those periods where the noise levels are, in fact, potentially dangerous.

I have had to deal with this problem often in my work at the Fed, where some data over decades is available on a weekly basis. One trick that works, besides averaging the data, is thinning out the stroke of the line so the overlaps do not appear so thick. It could make it difficult to read, but it avoids the density issues at the beginning of that chart.

All in all, though, I would love a London-like transport system here in Philly. I’d rather some loud noises than polluting cars on the road.

Credit for the piece goes to the Economist Data Team.

From Frying Pan to the Fires of a War Zone

Moving away from climate change now, we turn to the lovely land of Afghanistan. While the Trump administration continues to negotiate with the Taliban in hopes of ending the war, the war continues to go worse for Afghanistan, its government, and its allies, including the United States.

It is true that US and NATO ally deaths are down since the withdraw of combat troops in 2014. But, violence and sheer deaths are significantly up. And as this article from the Economist points out, the deaths in Afghanistan are now worse than they are in Syria.

The beginning of the article uses a timeline to chart the history of Afghan conflicts as well as the GDP and number of deaths. And it is a fascinating chart in its own right. But I wanted to share this, a small multiples featuring graphic looking at the geographic spread of deaths throughout the country.

Getting hotter (because red obviously means heat)
Getting hotter (because red obviously means heat)

It does a nice job by chunking Afghanistan into discrete areas shaped as hexagons and bins deaths into those areas. All the while, the shape remains roughly that of Afghanistan with the Hindu Kush mountain range in particular overlaid. (Though, I am not sure why it is made darker in the 2003–04 map.)

To highlight particular cities or areas, hexagons are outlined to draw attention to the population centres of interest. But overall, the rise in violence and deaths is clear and unmistakable. And it has spread from what was once pockets in the south to the whole of the country that isn’t mountains or deserts.

Tamerlane would be proud.

Credit for the piece goes to the Economist graphics department.

Hotter Muggier Faster

Last week we looked at a few posts that showed the future impact of climate change at both a global and US-level scale. In the midst of last week and those articles, the Washington Post looked backwards at the past century or so to identify how quickly the US has changed. Spoiler: some places are already significantly warmer than they have been. Spoiler two: the Northeast is one such place.

The piece is a larger and more narrative article using examples and anecdotes to make its point. But it does contain several key graphics. The first is a big map that shows how temperature has changed since 1895.

The Southeast is an anomaly, but its warming has accelerated since the 1960s
The Southeast is an anomaly, but its warming has accelerated since the 1960s

The map does what it has to and is nothing particularly fancy or groundbreaking—see what I did there?—in design. But it is clear and communicates effectively the dramatic shifts in particular regions.

The more interesting part, along with what we looked at last week, is the ability to choose a particular county and see how it has trended since 1895 and compare that to the baseline, US-level average. Naturally, some counties have been warming faster, others slower. Philadelphia County, the entirety of the city, has warmed more than the US average, but thankfully less than the Northeast average as the article points out.

This ain't so good
This ain’t so good

But, not to leave out Chicago as I did last week, Cook County, Illinois is right on line with the US average.

Nor is this, but it's average
Nor is this, but it’s average

But the big cities on the West Coast look very unattractive.

Tinseltown is out of the question
Tinseltown is out of the question

The interactive piece does a nice job clearly focusing the user’s attention on the long run average through the coloured lines instead of focusing attention on the yearly deviations, which can vary significantly from year to year.

And for those Americans who are not familiar with Celsius, one degree Celsius equals approximately 1.8º Fahrenheit.

Overall this is a solid piece that continues to show just what future generations are going to have to fix.

Credit for the piece goes to Steven Mufson, Chris Mooney, Juliet Eilperin, John Muyskens, and Salwan Georges.

How Warm Will It Get? Part II

Yesterday we looked at a nice piece from the BBC showing how big cities across the world will warm from the impact of climate change. It did a really nice job of showcasing the numbers. But it was admittedly number heavy. (And for the Americans in my audience, you probably were left out in the…cold…because the rest of the world uses Celsius to talk temperature.)

But this piece from the University of Maryland is something I have been raving about for weeks now. Generally speaking, people are able to better internalise data and information when they can compare it to something tangible or familiar. And degrees of Celsius, whilst accurate, fail to do that. So this piece takes their 2080 forecast and compares it to today, but in terms of place.

Ew. Just eeww.
Ew. Just eeww.

The above map is for Philadelphia. It shows how by 2080, according to a current emissions model, the city’s climate will best resemble that of Memphis, Tennessee and the lower Mississippi River Valley. Or, similar to the tidal regions of North Carolina. Having been to Memphis in the summer once, none of those are pleasant comparisons.

And for those of you in Chicago, it does not get a whole lot better.

Not as ew-y. But still ew.
Not as ew-y. But still ew.

So while these might not be as bad, it still is a swath of the plains and the lower Ohio River Valley. And…yes, a little like today’s climate here in Philadelphia.

From a design standpoint, I probably would have used a light or greyed out map. The colours used to represent the topography are too similar to those used to define the similarity. And that can make it tricky to read.

But the true strength of this piece is the designers’ ability to link tomorrow’s climate to today’s by use of space. And as I said at the beginning, I have been talking about this piece offline for weeks. And I likely will for weeks to come.

Credit for the piece goes to Matthew C. Fitzpatrick and Robert R. Dunn .