Like I said yesterday, I wanted to compare cities, surprise, Philadelphia vs. Chicago. And so with some extra time I was able to finish this graphic that took the data from Climate Central to compare the two cities.
What you can see below is that Philadelphia has seen more significant temperature growth in both summer highs and summer lows. And, importantly, the increase in low temperatures, i.e. nighttime, has been greater than that of daytime highs. That means that we have less of an opportunity to cool down after a hot summer day, adding stress to the system.
Chicago on the other hand has seen less overall growth, though it’s still present. And there too we see the same pattern of greater increases in low, i.e. nighttime, temperatures than of daytime highs.
It’s remarkable to think that the flat where I lived seven of my eight years in Chicago had no air conditioning unit in the bedroom, only in the living room. It was, of course, an older concrete building from the 1960s/70s when, as the chart above shows, nighttime temperatures didn’t really require air conditioning.
But like I said yesterday, I’m just glad I’ve been able to crank the air conditioning the last several days.
First, I should say that I don’t have a lot to say about this graphic because I went back to the source because I was interested in another city and I wanted to compare the two. In other words, expect a small graphic follow up to this maybe tomorrow.
Anyways, over the last few years since returning to Philadelphia after eight years away in Chicago, I’ve had numerous conversations with different people about how “I don’t remember it always being this hot before”, which is particularly relevant as the Philadelphia region endures excessive heat. Thankfully, it’s not nearly as bad as the Pacific Northwest. Also I have air conditioning blasting next to me as I type this out, so, you know.
The common refrain in these conversations, however, tends to be less about how we have high temperatures and more about how it’s difficult to sleep at night. And there’s a reason for that as this article from the Philadelphia Inquirer explains, our average summer low temperatures are rising, and rising faster than our average summer high temperatures.
Of course you can probably already see where I was going with this. The Inquirer linked to their source and that’s where I’ve spent my time this morning, alas, I didn’t quite have enough to finish what I started and so this post will have to do.
Credit for the piece goes to the Philadelphia Inquirer graphics department.
Thankfully today’s forecast calls for cooler temperatures. Your author is not a fan of hot weather, which means being outside in summer is…less than ideal. It also means that the air conditioner runs frequently and on high for a few months. (Conversely, I can probably count on one hand the number of times I turned on the heat this winter.)
The problem is, the two biggest contributors to US carbon emissions? Heating/cooling and transport. In other words, heating your home in the winter, cooling it in the summer, and then driving your non-electric vehicle.
After the recent heatwave in New England, the Boston Globeexamined the impact of the heatwave on the environment. The article led with the claim it used four charts to do so. I quibble with that distinction because this is a screenshot of the second graphic.
I mean, it’s not prose text. Rather, we have three factettes paired with illustrations. At the top of this post, I mentioned the impact of transport for a reason. In an ideal world, in order to get carbon emissions under control one of the changes we would need to see is getting people out of their personal automobiles and into mass transit. Subways and light rail are far cleaner and can actually be cheaper for households than car ownership. And so we should be encouraging their use and building more of them.
Look above and you’ll see an icon of a subway car. Except it’s not. The graphic/factette is actually talking about rail cars full of coal that transport fuel from mine to generating station. Those look more like this, from James St. James via Wikimedia Commons.
Small, subtle details matter. And so I’d propose a new icon that tries to capture the industrial coal train, ideally something that I spent more than five minutes on.
But it breaks the linkage between passenger train and coal train, which is not ideal for the purposes of an article highlighting the environmental impacts of US households.
That all said, the article did a really good job with the other graphics it used. My favourite was this chart, decidedly not a combination chart.
It looks at the correlation between high temperatures and energy usage. But, instead of lazily throwing the temperatures atop the bars, the designers more carefully placed them below the energy usage chart. The top chart should look familiar to those who have been following my Covid-19 charts, a daily number that then has the rolling seven-day average plotted above it to smooth out any one-day quirks. The designer then chose to highlight the heatwave in red.
For temperatures, I like the overall approach. But I wonder if a more nuanced approach could have taken the graph a step farther to excellent. Presently we have a single red line representing daily average high temperature. But in the plot above we use red to indicate the heat wave of early June, five consecutive days of temperatures in excess of 90ºF. What if that line were black or grey or some neutral colour, and then only the heatwave was coloured in red? It would more clearly link the two together. And it avoids the trap of red implying heat, when you need to only go back to late May when the East Coast had early spring like temperatures near 50ºF, decidedly not red on a temperature scale.
Overall, though, it’s refreshing to see a thoughtful approach taken here instead of the usual slapdash throw one chart atop the other.
And the rest of the article uses restrained, smart graphics as well. Bar charts and small multiples to capture air pollution and EMS calls. You should read the full article for the insights and the feedback loops we have.
After all, it’s not that the heating/cooling is itself the problem, especially since the removal of CFCs since the Montreal Protocol in 1987 that banned those pesky chemicals that harm the ozone layer—remember when that was the big environmental issue in the 1990s? The issue is how we generate the electricity that powers the heating/cooling systems—and if you want to use electric cars, whence comes their electric charge—as if we’re using coal plants, that just exacerbates the problem. But if we use carbon-less plants, e.g. nuclear, solar, or wind, we’re not generating carbon emissions.
Two Fridays ago, I opened the door and found my copy of the New York Times with a nice graphic above the fold. This followed the announcement from the White House of aggressive targets to reduce greenhouse gas emissions
In general, I love seeing charts and graphics above the fold. As an added bonus, this set looked at climate data.
But there are a few things worth pointing out.
First from a data side, this chart is a little misleading. Without a doubt, carbon dioxide represents the greatest share of greenhouse gasses, according to the US Environmental Protection Agency (EPA) it was 76% in 2010. Methane contributes the next largest share at 16%. But the labelling should be a little clearer here. Or, perhaps lead with a small chart showing CO2’s share of greenhouse gasses and from there, take a look at the largest CO2 emitters per person.
Second, where are the axis labels?
I will probably have more on this at a later date, but neither the bar chart nor the line charts have axis labels. Now the designers did choose to label the beginning value for the lines and the bars, but this does not account for the minimums or maximums. (It also assumes that the bottom of the lines is zero.)
For example, we can see that China began 1990 with emissions at 3.4 billon metric tons. The annotation makes clear that China’s aggregate emissions surpassed those of the US in 2004. But where do they peak? What about developing countries?
If I pull out a ruler and draw some lines I can roughly make some height comparisons. But, an easier way would be simply to throw some dotted lines across the width of the page, or each line chart.
This piece takes a big swing at presenting the challenge of reducing emissions, but it fails to provide the reader with the proper—and I think necessary—context.
Credit for the piece goes to Nadja Popovich and Bill Marsh.
I remember hearing and reading stories as a child about the Thames in London freezing over and hosting winter festivals. Of course most of that happened during what we call the Little Ice Age, a period of below average temperatures during the 15th through the early 19th century.
But those days are over.
The UK’s Meteorological Office, or the Met for short, released some analysis of the impacts of climate change to winter temperatures in the United Kingdom. And if, like me, you’re more partial to winter than summer, the news is…not great.
Broadly speaking, winters will become warmer and wetter, i.e. less snowy and more rainy. Meanwhile summers will become hotter and drier. Farewell, frost festivals.
But let’s talk about the graphic. Broadly, it works. We see two maps with a unidirectional stepped gradient of six bins. And most importantly those bins are consistent between the maps, allowing for the user to compare regions for the same temperatures: like for like.
But there are a couple of things I would probably do a bit differently. Let’s start with colour. And for once we’re not dealing with the colour of the BBC weather map. Instead, we have shades of blue for the data, but all sitting atop an even lighter blue that represents the waters around the UK and Ireland. I don’t think that blue is really necessary. A white background would allow for the warmest shade of blue, +4ºC, to be even lighter. That would allow greater contrast throughout the spectrum.
Secondly, note the use of think black lines to delineate the sub-national regions of the UK whilst the border of the Republic of Ireland is done in a light grey. What if that were reversed? If the political border between the UK and Ireland were black and the sub-national region borders were light grey—or white—we would see a greater contrast with less visual disruption. The use of lines lighter in intensity would allow the eye to better focus on the colours of the map.
Then we reach an interesting discussion about how to display the data. If the purpose of the map is to show “coldness”, this map does it just fine. For my American audience unfamiliar with Celsius, 4ºC is about 39ºF, many of you would definitely say that’s cold. (I wouldn’t, because like many of my readers, I spent eight winters in Chicago.)
The article touches upon the loss of snowy winters. And by and large, winters require temperatures below the freezing point, 0ºC. So what if the map used a bidirectional, divergent stepped gradient? Say temperatures above freezing were represented in shades of a different colour like red whilst below freezing remained in blue, what would happen? You could easily see which regions of the UK would have their lowest temperatures fail to fall below freezing.
Or another way of considering looking at the data is through the lens of absolute vs. change. This graphic compares the lowest annual temperature. But what if we instead had only one map? What if it coloured the UK by the change in temperature? Then you could see which regions are being the most (or least) impacted.
If the data were isolated to specific and discrete geographic units, you could take it a step further and then compare temperature change to the baseline temperatures and create a simple scatterplot for the various regions. You could create a plot showing cold areas getting warmer, and those remaining stable.
That said, this is still a really nice piece. Just a couple little tweaks could really improve it.
The last two days we looked at densification in cities and how the physical size of cities grew in response to the development of transport technologies, most notably the automobile. Today we look at a New York Times article showing the growth of automobile emissions and the problem they pose for combating the greenhouse gas side of climate change.
The article is well worth a read. It shows just how problematic the auto-centric American culture is to the goal of combating climate change. The key paragraph for me occurs towards the end of the article:
Meaningfully lowering emissions from driving requires both technological and behavioral change, said Deb Niemeier, a professor of civil and environmental engineering at the University of Maryland. Fundamentally, you need to make vehicles pollute less, make people drive less, or both, she said.
Of course the key to that is probably in the range of both.
The star of the piece is the map showing the carbon dioxide emissions on the roads from passenger and freight traffic. Spoiler: not good.
Each MSA is outlined in black and is selectable. The designers chose well by setting the state borders in a light grey to differentiate them from when the MSA crosses state lines, as the Philadelphia one does, encompassing parts of Pennsylvania, New Jersey, Delaware, and Maryland. A slight opacity appears when the user mouses over the MSA. Additionally a little box remains up once the MSA is selected to show the region’s key datapoints: the aggregate increase and the per capita increase. Again, for Philly, not good. But it could be worse. Phoenix, which surpassed Philadelphia proper in population, has seen its total emissions grow 291%, its per capita growth at 86%. My only gripe is that I wish I could see the entire US map in one view.
The piece also includes some nice charts showing how automobile emissions compare to other sources. Yet another spoiler: not good.
Since 1990, automobile emissions have surpassed both industry emissions and more recently electrical generation emissions (think shuttered coal plants). Here what I would have really enjoyed is for the share of auto emissions to be treated like that share of total emissions. That is, the line chart does a great job showing how auto emissions have surpassed all other sources. But the stacked chart does not do as great a job. The user can sort of see how passenger vehicles have plateaued, but have yet to decline whereas lorries have increased in recent years. (I would suspect due to increased deliveries of online-ordered goods, but that is pure speculation.) But a line chart would show that a little bit more clearly.
Finally, we have a larger line chart that plots each city’s emissions. As with the map, the key thing here is the aggregate vs. per capita numbers. When one continues to scroll through, the lines all change.
Very quickly one can see how large cities like New York have large aggregate emissions because millions of people live there. But then at a per capita level, the less dense, more sprawl-y cities tend to shoot up the list as they are generally more car dependent.
Credit for the piece goes to Nadja Popovich and Denise Lu.
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.
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.
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.
The UN climate summit begins in New York today. So let’s take a look at another data visualisation piece exploring climate change data. This one comes from a Washington Post article that, while largely driven by a textual narrative, does make use of some nice maps.
There is nothing too crazy going on with the actual map itself. I like the subtle use here of a stepped gradient for the legend. This allows for a clearer differentiation between adjacent regions and just how, well, bad things have become.
But where the piece shines is about halfway through. It takes this same map and essentially filters it. It starts with those regions with temperature changes over 2ºC. Then it progressively adds slightly less hotter regions to the map.
It’s a nice use of scrolling and filtering to highlight the areas worst impacted and then move down the horrible impact scale. And because this happens in the middle of the piece, giving it the full column width (online) allows the reader to really focus on the impacts.
Credit for the piece goes to Chris Mooney and John Muyskens.
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.
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.
The G7 conference in France wrapped up yesterday and they announced an aid package for Brazil. Why? Because satellite data from both Brazil and the United States points to a rash of fires devastating the Amazon rainforest, the world’s largest carbon sink, or sometimes known as the lungs of the Earth. I have not had time to check this statistic, but I read that 1/5 the world’s oxygen comes from the Amazon ecosystem. I imagine it is a large percentage given the area and the number of trees, but 20% seems high.
Regardless, it is on fire. Some is certainly caused by drier conditions and lightning strikes. But most is manmade. And so after the Brazilian president Jair Bolsonaro said his country did not have the resources to fight the fires, the G7 offered aid.
This morning, Bolsonaro refused it.
And so we have this map from InfoAmazonia that takes NASA data on observed fires for all of South America. I cropped my screencapture to Brazil.
A key feature to note here, in addition to that black background approach, is that you will see three distinct features: yellow hotspots fading to cold black areas, yellow dots with red outlines, and red dots. Each means something different. The yellow to red to black gradient simply means frequency of fires, the yellow dots with red outlines represent significantly hot fires from 2002 through 2014. The red dots are what concern us. Those are fires within the last month.
Sure enough, we see lots of fires breaking out across the Amazon. And Bolsonaro not only rejected the aid, but a few weeks ago he rejected similar data. He fired the head of a government agency tasked with tracking the deforestation of the Amazon after he released the agency’s monthly report detailing the deforestation. It had risen by 39%.
From a design standpoint, it is a solid piece. I do wonder, however, if some kind of toggle for the three datasets could have been added. Given the focus on the new fires breaking out, isolating those compared to the historic fires would be useful.
But before wrapping up, I also want to point out that there are a significant number of red dots appearing outside Brazil. The Amazon exists beyond borders, and there are a significant number of fires in neighbouring Bolivia and Paraguay. Let alone around the world…