Choose Your Own FiveThirtyEight Adventure

In case you weren’t aware, the US election is in less than a week, five days. I had written a long list of issues on the ballot, but it kept getting longer and longer so I cut it. Suffice it to say, Americans are voting on a lot of issues this year. But a US presidential election is not like many other countries’ elections in that we use the Electoral College.

For my non-American readers, the Electoral College, very briefly, was created by the country’s founding fathers (Washington, Jefferson, Adams, Franklin, et al.) to do two things. One, restrict selection of the American president to a class of individuals who theoretically had a broader/deeper understanding of the issues—but who also had vested interests in the outcome. The founders did not intend for the American people to elect the president. The second feature of the Electoral College was to prevent the largest states from dominating smaller states in elections. Why else would Delaware and Rhode Island surrender their sovereignty to join the new United States if Virginia, Pennsylvania, and New York make all the decisions? (The founders went a step further and added the infamous 3/5 clause, but that’s another post.)

So Americans don’t elect the president directly and larger states like California, New York, and Texas, have slightly less impact than smaller states like Wyoming, Vermont, and Delaware. Each state is allotted a number of Electoral College votes and the key is to reach 270. (Maybe another time I’ll get into the details of what happens in a 269–269 tie.) Many Americans are probably familiar with sites like 270 To Win, where you can determine the outcome of the election by saying who won each state. But, even though the US election is really 50 different state elections, common threads and themes run through all those states and if one candidate or another wins one state, it makes winning or losing other states more or less likely. FiveThirtyEight released a piece that attempts to link those probabilities and help reveal how decisions voters in one state make may reflect on how other voters decide.

The interface is fairly straightforward—I’m looking at this on a desktop, though it does work on mobile—with a bunch of choices at the top and a choropleth map below. There we have a continually divergent gradient, meaning the states aren’t grouped into like bins but we have incredibly subtle differences between similar states. (I should also point out that Maine and Nebraska are the two exceptions to my above description of the Electoral College. They divide their votes by congressional district, whoever wins the district gets that Electoral College vote and then the state overall winner receives the remaining two votes.)

Below that we have a bar chart, showing each state, its more/less likely winner state and the 270 threshold. Below that, we have what I’ve read/heard described as a ball plot. It represents runs of the simulation. As of Thursday morning, the current FiveThirtyEight model says Trump has an 11 in 100 chance of winning, Biden, conversely, an 89-in-100 chance.

But what happens when we start determining the winners of states?

Well, for my non-American readers, this election will feature a large number of voters casting their ballots early. (I voted early by mail, and dropped my ballot off at the county election office.) That’s not normal. And I cannot emphasise this next point enough. We may not know who wins the election Tuesday night or by the time Americans wake up on Wednesday. (Assuming they’re not like me and up until Alaska and Hawaii close their polls. Pro-tip, there’s a potentially competitive Senate race in Alaska, though it’s definitely leaning Republican.)

But, some states vote early and/or by mail every year and have built the infrastructure to count those votes, or the vast majority of them, on or even before Election Day. Three battleground states are in that group: Arizona, Florida, and North Carolina. We could well know the result in those states by midnight on Election Day—though Florida is probably going to Florida.

So what happens with this FiveThirtyEight model if we determine the winners of those three states? All three voted for Trump in 2016, so let’s say he wins them again next week.

We see that the states we’ve decided are now outlined in black. The remainder of the states have seen their colours change as their odds reflect the set electoral choice of our three states. We also now have a rest button that appears only once we’ve modified the map. I’m also thinking that I like FiveyFox, the site’s new mascot? He provides a succinct, plain language summary of what the user is looking at. At the bottom we see what the model projects if Arizona, Florida, and North Caroline vote for Trump. And in that scenario, Trump wins in 58 out of 100 elections, Biden in only 41. Still, it’s a fairly competitive election.

So what happens if by midnight we have results from those three states that Biden has managed to flip them? And as of Thursday morning, he’s leading very narrowly in the opinion polls.

Well, the interface hasn’t really changed. Though I should add below this screenshot there is a button to copy the link to this outcome to your clipboard if, like me, you want to share it with the world or my readers.

As to the results, if Biden wins those three states, Trump has less than a 1-in-100 chance of winning and Biden a greater than 99-in-100.

This is a really strong piece from FiveThirtyEight and it does a great job to show how states are subtly linked in terms of their likelihood to vote one way or the other.

Credit for the piece goes to Ryan Best, Jay Boice, Aaron Bycoffe and Nate Silver.

Double Your Hurricanes, Double Your Fun

In a first, the Gulf of Mexico basin has two active hurricanes simultaneously. Unfortunately, they are both likely to strikes somewhere along the Louisiana coastline within approximately 36 hours of each other. Fortunately, neither is strong as a storm named Katrina that caused a mess of things several years ago now.

Over the last few weeks I have been trying to start the week with my Covid datagraphics, but I figured we could skip those today and instead run with this piece from the Washington Post. It tracks the forecast path and forecast impact of tropical storm force winds for both storms.

The forecast path above is straight forward. The dotted line represents the forecast path. The coloured area represents the probability of that area receiving tropical storm force winds. Unsurprisingly the present locations of both storms have the greatest possibilities.

Now compare that to the standard National Weather Service graphic, below. They produce one per storm and I cannot find one of the combined threat. So I chose Laura, the one likely to strike mid-week and not the one likely to strike later today.

The first and most notable difference here is the use of colour. The ocean here is represented in blue compared to the colourless water of the Post version. The colour draws attention to the bodies of water, when the attention should be more focused on the forecast path of the storm. But, since there needs to be a clear delineation between land and water, the Post uses a light grey to ground the user in the map (pun intended).

The biggest difference is what the coloured forecast areas mean. In the Post’s versions, it is the probability of tropical force winds. But, in the National Weather Service version, the white area actually is the “cone”, or the envelope or range of potential forecast paths. The Post shows one forecast path, but the NWS shows the full range and so for Laura that means really anywhere from central Louisiana to eastern Texas. A storm that impacts eastern Texas, for example, could have tropical storm force winds far from the centre and into the Galveston area.

Of course every year the discussion is about how people misinterpret the NWS version as the cone of impact, when that is so clearly not the case. But then we see the Post version and it might reinforce that misconception. Though, it’s also not the Post’s responsibility to make the NWS graphic clearer. The Post clearly prioritised displaying a single forecast track instead of a range along with the areas of probabilities for tropical storm force winds.

I would personally prefer a hybrid sort of approach.

But I also wanted to touch briefly on a separate graphic in the Post version, the forecast arrival times.

This projects when tropical storm force winds will begin to impact particular areas. Notably, the areas of probability of tropical storm force winds does not change. Instead the dotted line projections for the paths of the storms are replaced by lines relatively perpendicular to those paths. These lines show when the tropical storm winds are forecast to begin. It’s also another updated design of the National Weather Service offering below.

Again, we only see one storm per graphic here and this is only for Laura, not Marco. But this also probably most analogous to what we see in the Post version. Here, the black outline represents the light pink area on the Post map, the area with at least a 5% forecast to receive tropical storm force winds. The NWS version, however, does not provide any further forecast probabilities.

The Post’s version is also design improved, as the blue, while not as dark the heavy black lines, still draws unnecessary attention to itself. Would even a very pale blue be an improvement? Almost certainly.

In one sense, I prefer the Post’s version. It’s more direct, and the information presented is more clearly presented. But, I find it severely lack in one key detail: the forecast cone. Even yesterday, the forecast cone had Laura moving in a range both north and south of the island of Cuba from its position west of Puerto Rico. 24 hours later, we now know it’s on the southern track and that has massive impact on future forecast tracks.

Being east of west of landfall can mean dramatically different impacts in terms of winds, storm surge, and rainfall. And the Post’s version, while clear about one forecast track, obscures the very real possibilities the range of impacts can shift dramatically in just the course of one day.

I think the Post does a better job of the tropical storm force wind forecast probabilities. In an ideal world, they would take that approach to the forecast paths. Maybe not showing the full spaghetti-like approach of all the storm models, but a percentage likelihood of the storm taking one particular track over another.

Credit for the Post pieces goes to the Washington Post graphics department.

Credit for the National Weather Service graphics goes to the National Weather Service.

Red Sox Starting Rotation: A Dumpster Fire in a Dumpster Fire Year

Baseball for the Red Sox starts on Friday. Am I glad baseball is back? Yes?

I love the sport and will be glad that it’s back on the air to give me something to watch. But the But the way it’s being done boggles the mind. Here today I don’t want to get into the Covid, health, and labour relations aspect of the game. But, as the title suggests, I want to look at a graphic that looks at just how bad the Red Sox could be this (shortened) year. And over at FiveThirtyEight, they created a model to evaluate teams’ starting rotations on an ongoing basis.

The Red Sox are just bad.
Look at the Red Sox, one of the worst in baseball.

Form wise, this isn’t too difficult than what we looked at yesterday. It’s a dot plot with the dots representing individual pitchers. The size of the dots represents their number of total starts. This is an important metric in their model, but as we all know size is a difficult attribute for people to compare and I’m not entirely convinced it’s working here. Some dots are clearly smaller than others, but for most it’s difficult for me to clearly tell.

Colour is just tied to the colour of the teams. Necessary? Not at all. Because the teams are not compared on the same plot, they could all be the same colour. If, however, an eventual addition were made that plot the day’s matchups on one line, then colour would be very much appropriate.

I like the subtle addition of “Better” at the top of the plots to help the user understand the constructed metric. Otherwise the numbers are just that, numbers that don’t mean anything.

Overall a solid piece. And it does a great job of showing just how awful the Red Sox starting rotation is going to be. Because I know who Nate Eovaldi is. And I’ve heard of Martin Perez. Ryan Weber I only know through largely pitching in relief last year. And after that? Well, not on this graphic, but we have Eduardo Rodriguez who had corona and, while he has recovered, nobody knows how that will impact people in sports. There’s somebody named Hall who I have never heard of. Then we have Brian Johnson, a root for the guy story of beating the odds to reach the Major Leagues but who has been inconsistent. Then…it is literally a list of relief pitchers.

We dumped the salary of Mookie Betts and David Price and all we got was basically a tee-shirt saying “We still need a pitcher or three”.

Credit for the piece goes to Jay Boice.

A Map of Unequal Comparisons

I’ve largely been busy creating and posting content on the Covid pandemic and its impact on the Pennsylvania, New Jersey, and Delaware tristate area along with, by request, both Virginia, and Illinois, my former home. It leaves me very little time for blogging, and I really do not want this site to become a blog of my personal work. That’s why I have a portfolio or my data project sites, after all.

But in posting my Covid datagraphics, I’ve come across variations of this map with all sorts of meme-y, witty captions saying why Canada is doing so much better than the US, why Americans shouldn’t be allowed to travel to Canada, and now why the Blue Jays shouldn’t be allowed to host Major League Baseball games.

Wait just a minute, there…

Well, that map isn’t necessarily wrong, but it’s incredibly misleading.

First, the map comes from the fantastic Johns Hopkins work on Covid-19. (Full disclosure, that’s the data source I use at work to create my work work datagraphics: And their site has a larger and more comprehensive dashboard (still hate that term but it does have sticking power) of which the map is the focal point.

The numbers as of this posting.

You can see the map there in the centre and some tables to the left, some tables to the right, and even a micro table beneath thundering away at the map’s position. I could get into the overall design—maybe I will one of these days—but again, let’s look at that map.

The crux of the argument is that there are a lot of red dots in the United States and very few in Canada. But look at the table in the dashboard on the left. At the very bottom you see three small tabs, Admin 0, Admin 1, and Admin 2. Admin 0 contains all entities at the sovereign state level, e.g. US, Canada, Sweden, Brazil, &c. Admin 1 is the provincial/state level, e.g. Pennsylvania, Illinois, Ontario, Quebec, &c. Admin 2 is the sub-provincial/sub-state level, e.g. Philadelphia County, Cook County, Chester County, Lake County, &c.

Notice anything about my examples? Not all countries have provinces/states, but Canada certainly does. And then at Admin 2, the examples and indeed the data only have US counties and US data. Everything in Canada has been aggregated up to Admin 1. And that is the problem.

The second part to point out is the dot-ness of the map. And to be fair, this is part of a broader problem I have been seeing in data visualisation the last few months. Dots, circles, or markers imply specificity in location. The centre of that object, after all, has to fall on a specific geographic place, a latitude and longitude coordinate. It utterly fails to capture the dimensions and physical size of the geographic unit, which can be critical.

Because not all geographic units are of the same size. We all know Rhode Island as one of the smallest US states. Let’s compare that to Nunavut or Yukon in Canada, massive provinces that spread across the Canadian Arctic. Rhode Island, according to Google, 1212 square kilometres. Nunavut? 808,200.

So now show both states/provinces on a map with one dot and Rhode Island’s will practically cover the state. And it will also be surrounded by and in close proximity to the states or Massachusetts and Connecticut. Nunavut, on the other hand will be a small dot in a massive empty space on a map. But those dots are equal.

Now, combine that with the fact that the Hopkins map is showing data on the US county level. Every single county in the United States gets a red dot. By default, that means the US is covered with red dots. But there is no county-level equivalent data for Canada. Or for Mexico (also seen in the above graphic). And so given we’re only using dots to relate the data, we see wide swaths of empty space, untouched by red dots. And that’s just not true.

Yes, large parts of the Canadian Arctic are devoid of people, but not southern Ontario and Quebec, not the southwestern coast of British Columbia, not the Maritimes.

The Hopkins map should be showing geographic units at the same admin level. By that I mean that when on Admin 0, the map should reflect geographic units of sovereign state level, allowing us to compare the US to Canada directly. But, and for this argument I’m assuming we’re keeping the dots despite their flaws, we only see Admin 0 level data.

Admin 1 shows only provincial level data. Some countries will begin to disappear, because Hopkins does not have the data at that level. But in North America, we still can compare Pennsylvania and Illinois to Ontario and Quebec.

But then at Admin 2, we only see the numerous dots of the United States counties. It’s neither an accurate nor a helpful comparison to contrast Chester County or Will County to the entire province of Ontario and so the map should not allow it. Instead, as the above graphic shows, it creates misconceptions of the true state of the pandemic in the US and Canada.

Credit for the Hopkins dashboard goes to, well, Hopkins.

Modelling the Impact of Not Sheltering in Place or Staying at Home

The administration botched the early stages of the COVID-19 pandemic. Only within the last two weeks have states acted to begin enacting dramatic policies aimed at slowing the spread of the virus through their communities. But what policies the federal government has enacted are now threatened by an administration that prioritises the economy and market over the lives of the citizens it leads.  The White House is discussing loosening all the policies of social distancing that health officials and scientists say are necessary to slow the spread of the virus.

This website from uses a model to predict the impact state by state of various policies on hospital overcrowding and ultimately deaths. The site opens with a map of the United States showing, broadly, what kind of response each state has followed (understanding things change rapidly these days).

The state of reactions in the United States
The state of reactions in the United States

That also serves as the navigation for a deep dive into those models for that state. Here I have selected my home state of Pennsylvania. It borders New Jersey and New York, two states that revolve, at least in part, around New York City, rapidly becoming the epicentre of the US outbreak, supplanting Seattle and the Pacific Northwest. What would the state face if we allowed things to keep going blithely on? What would happen if we merely socially distance for three months? What if we shelter in place for three months? (Emphasis added by me to show this is a long-term problem.)

Potential outcomes for Pennsylvania
Potential outcomes for Pennsylvania

Turns out that things don’t work out that well if we don’t stay at home, stop travelling, stop socialising. A table below the line charts shows the user how bad things go for the state in a table.

A table of potential outcomes
A table of potential outcomes

As you can see, for Pennsylvania, if we were to continue going on like normal, that would result in the deaths of almost the size of the entire city of Pittsburgh. Imagine if the city of Pittsburgh were suddenly wiped off the state map. That’s the level we are talking about.

Just three months of just social distancing? Well now you’re talking about wiping out just the cities of Allentown and Scranton.

Sheltering in place for three months, statewide? Well, thankfully Pennsylvania has lots of towns around the size of 5000 to choose from. Imagine no more Paoli, or Tyrone. Or maybe a Collegeville or Kutztown. An Oxford or a Media. Pick one of those and wipe it from the map.

Fundamentally the choice comes down to, do you want to restart your economy or do you want to save lives? Saving lives will undoubtedly mean unemployment, shattered 401k plans, bankruptcies, mental health problems, and cities, towns, and industries devastated without a tax base to provide for the necessary services. But, saving those jobs and dollars will means tens if not hundreds of thousands of deaths.

I don’t envy the state executive branches making these decisions.

Pennsylvania has chosen a middle road, if you will. It enacted a stay-at-home policy for seven counties: Allegheny (Pittsburgh); Philadelphia and its suburban counties of Bucks, Chester, Delaware, and Montgomery; and Monroe County. The rest of the state, primarily where the virus has yet to make any real significant appearance or appears to be spreading in the community, is not under the strictest of measures. This site’s model doesn’t account for a partial, statewide stay-at-home, but Pennsylvania’s choice is clearly a far superior one for people who prioritise lives over dollars.

Finally, to the people I have seen from my apartment gathering in parks, partying in outdoor spaces, that I can hear throwing house parties, please stop. If not for you, for the rest of us.

Credit for the piece goes to

Americans Can’t Kick the Auto Habit

After looking this week at the growth of the physical size of cities due to improvements in transport technologies, the increasing density of cities, and then the contribution of automobile (especially personal cars) to carbon dioxide emissions, today we look at a piece from the Transport Politic comparing US and French mass transit ridership to see whether the recent decline in US ridership is inevitable or a choice made by consumers and policymakers. Spoiler: it’s not inevitable.

The article makes use of a few graphics and an interactive component. The lead-in graphic is a nice line chart that runs with the spaghetti nature of the graphic: lots of line but only two are really highlighted.

The French are definitely better than the US here.
The French are definitely better than the US here.

Light grey lines and light blue lines encode the US and French cities under study. But only the lines representing the averages of both the US and France are darkly coloured and in a thicker stroke to stand out from the rest. Normally I would not prefer the minimum of the y-axis being 50%, but here the baseline is actually 100% so the chart really works well. And interestingly it shows that prior to the Great Recession, the United States was doing better than France in adoption of mass transit, relative to 2010 numbers.

But then when you directly compare 2010 to 2018 for various US and French cities, you get an even better chart. Also you see that French cities reclaim the lead in transit growth.

A lot of declines on this side of the pond.
A lot of declines on this side of the pond.

These two static graphics, which can each be clicked to view larger, do a really great job of cutting through what some might call noise of the intervening years. I do like, much like yesterday’s post, the comparison of total or aggregate ridership to per capita numbers. It shows how even though New York’s total ridership has increased, the population has increased faster than the ridership numbers and so per capita ridership has declined. And of course as yesterday’s post examined, in the States the key to fighting climate change is reducing the number of people driving.

What I cannot quite figure out from the graphic is what the colouration of the lines mean. I thought that perhaps the black vs. grey lines meant the largest cities, but then LA would be black. Maybe for the steepest declines, but no, because both LA and Boston are grey. I also thought the grey lines might be used when black lines overlap to aid clarity, but then why is Boston in grey? Regardless, I like the choice of the overall form.

But where things go really downhill are the interactive charts.

Just what?
Just what?

Talk about unintelligible spaghetti charts. So the good. The designer kept the baseline at 100% and set the min and max around that. After that it’s a mess. Even if the colours all default to the rainbow, the ability to select and isolate a particular city would be incredibly valuable to the user. Unfortunately selecting a city does no such thing. All the other cities remain coloured, and sometimes layered atop the selected city.

I would have thrown the unselected cities into the greyscale and let the selected city rise to the top layer and remain in its colour. Let it be the focus of the user’s attention.

Or the designer could have kept to the idea in the first graphic and coloured American cities grey and French cities light blue and then let the user select one from among the set and compare that to the overall greyed/blued masses and the US and French averages.

Overall, it wasn’t a bad piece. But that final interactive bit was questionable. Unfortunately the piece started strong and ended weak, when the reverse would have been preferable.

Credit for the piece goes to Yonah Freemark.

Auto Emissions Stuck in High Gear

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.

From this I blame the Schuylkill, Rte 202, the Blue Route, I-95, and just all the highways
From this I blame the Schuylkill, Rte 202, the Blue Route, I-95, and just all the highways

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.

I've got it: wind-powered cars with solar panels on the bonnet.
I’ve got it: wind-powered cars with solar panels on the bonnet.

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.

Lots of people means lots of emissions.
Lots of people means lots of emissions.

There's driving in the Philadelphia area, but it's not as bad as it could be.
There’s driving in the Philadelphia area, but it’s not as bad as it could be.

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.

The Amazon Burns

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.

You should also see the smoke maps
You should also see the smoke maps

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…

Credit for the piece goes to

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 .