Filmed in Copenhagen October 2021. I talk about the power of maps to reveal the invisible, drawing examples from history as well as my co-authored books: Atlas of the Invisible, Where the Animals Go and London: The Information Capital.
In 2018 Oliver Uberti and I had the honour of receiving the Corlis Benefideo Award for Imaginative Cartography. What follows is an adaptation of our acceptance speech from the 2018 NACIS Annual Meeting in Norfolk, Virginia. To read the article in Cartographic Perspectives and for referencing information click here.
Oliver: Good evening. In preparing our remarks, James and I sought to learn more about Corlis Benefideo. For those of you who don’t know him, he’s the mysterious cartographer at the heart of Barry Lopez’s short story, “The Mappist,” who heroically clings to his cartographic principles to the chagrin of his employers. Perhaps you can relate.Fictional as Benefideo may be, the values his character embodies are real: curiosity, dedication, imagination. So I must say how delighted we are that NACIS created an award to celebrate imagination. Serious imagination. In the serious world of adults, imagination is often relegated to the realm of children, as if we’ve outgrown its utility. We couldn’t disagree more.
Imagination can be a way in. For the cartographer, an imaginative approach can clear a path into a particularly thorny dataset. For readers, it can open a door to understanding—suggesting what the map is about before they read a single word.
But what does it mean to be imaginative? If you agree with the Oxford English Dictionary that it means “having or showing creativity,” then we must also define “creativity.” For us, creativity is an exercise in surprise. It’s about making unexpected connections. Unexpected conversions. This is like that. This becomes that.
For example, what if we took some data on life satisfaction, sense of purpose, happiness, and anxiety and converted each metric into a different facial attribute? Now what if we combined them and applied this conversion to all thirty-three of London’s boroughs? Numbers becomes faces become a map.
To be imaginative then, to make unexpected connections, you need to maintain a deep visual inventory of things to connect. We call this “keeping the well full.” We go to art museums, travel, read, window shop, browse historical atlases and interior design catalogs, you name it. You never know when a visual reference will come in handy. So we’re always looking, looking, looking. The idea for our map showing how to visit all 270 of London’s Tube stations in a single day came while looking at a piece of art in Russell Square station not far from James’s office.
For another graphic, we had a list of haunted locations around London that we wanted to map. So I said, “OK brain, what’s something visual and modular that involves ghosts?” This soon escalated to correspondence with the Pac-Man permissions teams at Namco in Japan. Upon submitting a draft, Namco informed us that “pretzel and pear cannot be used. Please [replace with] apple and strawberry.” We replied with an amended design, to which we received the following: “Stems of apple and cherry have to be brown (please refer to the design guide for the exact color).” I took this as a good reminder that even when you’re being imaginative, details matter. They’re what make the metaphor convincing.
I did not always have the courage to make such imaginative leaps, at least not with cartography. I recall one of the first “map meetings” I attended as an entry-level designer at National Geographic. Senior designers, photo editors, text editors, researchers, and some of the top cartographers in the world were huddled around a map of Phoenicia. I noticed an opportunity to use color to link some labels and arrows, but I was just 23 years old, fresh out of school. Who was I to speak up?
I studied fine arts, not geography. For most of my life, maps were prompts for my imagination, not products of it. The earliest was a wooden puzzle of the fifty United States. Assembling those colored pieces over and over as a child induced a lasting synesthesia. To this day, when I imagine a state I see it as the color it was in that puzzle. California is lime; Michigan a sky blue; my home state of Pennsylvania, a chocolaty brown.
The memory of those shapes proved useful many years later during a pitch meeting at the magazine. The story team was marveling at a video in which Senator Al Franken drew a map of the US from memory. To me, this was not such a big deal. “I can do that,” I said. I was not believed. A pen and a napkin were fetched, and I proceeded to replicate the wooden puzzle forever engraved in my mind. My colleagues found this demonstration remarkable enough to put it in the magazine. What was remarkable from my perspective was that they could not see what I saw.
Differences in how we see the world are what make the world interesting. They’re also what make our work interesting. These differences arise from our range of life experiences—where we’re from, where we’ve gone, what we’ve read, what we like, who we love, even those unfortunate events that happen to us. But in our professional lives—in companies, universities, and newsrooms—differences can be discouraged in favor of “the way we’ve always done things around here.” We submit that it is precisely our imaginative differences that we must get into our work.
A few months after the napkin map, the magazine was preparing a special issue on the world’s water crisis. A story was proposed on an EPA study that had found trace amounts of pharmaceuticals in fish in watersheds around the country. I was asked if I had an idea of how we might visualize it. “Easy,” I said. “We’ll make a fish out of pills.”
I had begun to trust my imaginative instincts. And when those instincts told me to leave the magazine, to rent a small house in Michigan, and to begin collaborating on a book of maps with a geographer named James, I listened.
James: My route to mapping came from collecting what I now know to be spatial data. As a teenager, I obsessed over mapping technology, specifically the Casio Pathfinder wristwatch. It had a compass in it. And a barometer. And an altimeter. It had enough memory to store twelve hours of temperature and altitude data. On family hiking holidays, I would provide near-continuous updates on the drop in temperature due to the environmental lapse rate as we walked uphill.
From moving weather fronts to human migration, I’ve always been curious to know how the world worked and how we could navigate through it. Geography offered answers. However, it wasn’t until years later that I discovered my passion for mapping them. Even as an undergraduate I didn’t set out to make maps. I was driven by collecting and analyzing spatial data. My first real passion for this came from glaciology, and mapping shrinking glaciers in Iceland and Alaska. I then hit a stumbling block. Glaciology quickly became physics—and I’m no good at physics. Still, the methods I had used in my fieldwork, such as geostatistics, gave me a framework for conceptualizing the world in data points. They’d also shown me how maps can bring mountains alive. So with an itch for mapmaking and still no real clue about physics, I was drawn towards GIS and population mapping. That choice ultimately led Oliver and I to cross paths.
His world of design and my world of mapping first united in 2010 when Oliver was still at National Geographic. I was studying for a PhD that focused on the geographic patterns of millions of surnames in Europe and beyond. Oliver sought help to produce a map of the most popular surnames in the US. When the map was nominated for an award in London in September 2012, Oliver flew over and stopped by my office to say hello. The success of the surnames map had got me thinking, what if we took all the data we could find—on happiness, house prices, art, violent crime, and life expectancy—and created a new visual guide to my home city for the twenty-first century?
London: The Information Capital captured a moment in the city’s history. It was still on a high from the Olympics; the UK had just conducted the most detailed Census to date; more and more of its datasets were being released; and maps were more popular than ever thanks to the internet and a number of hugely successful exhibitions. We now realize that we were just starting to see the power of maps when combined with the promise of big and open data. Since the book was published in 2014, we have seen tremendous progress in this space to the point where cartography can sometimes seem like an arms race to map the world quicker and in more detail than ever before.
To keep up, it helps to have a collaborator. In many researcher-designer relationships, the designer comes in at the end. For us, the mapmaking process is an equal partnership from conception to the final map. When we find a promising topic or dataset, typically we’ll start by discussing the editorial angle we want to take and possible projections or visual metaphors that could help us frame the story. I will produce initial exports. Oliver will then test crops and color palettes, and I will re-export the data accordingly. It’s a constant back and forth.
For example, this graphic shows the origin-destination flows of commuters in Southern England. In R, I drew slightly transparent lines between where people live and where they work. I thought my export looked pretty good. Then in Illustrator, Oliver swapped my black background for blue and applied effects to make the lines glow.
A graphic that scales rectangles by the number of works each artist has in the Tate galleries started life as a basic treemap. Oliver then painstakingly broke the export into its constituent parts, transformed the rectangles into picture frames and sculptures, and arranged them salon-style in “the gallery.” Turner is the artist with by far the most works in the Tate so we licensed one of his paintings to fill the biggest box. What could have been an uninspiring collection of boxes is instantly brought alive for the reader.
For our second book, Where the Animals Go, our collaboration expanded to include scientists all over the globe who were using new technology to track the movements of animals in unprecedented detail. This experience taught us that it’s not fair to expect everyone who creates or analyzes spatial data to be cartographers. Many scientists create maps with an academic publication in mind and then move on without giving them a second glance. Others are nervous of sharing data with those outside their field for fear of their work being misrepresented or used without credit. We may disagree with these perspectives, but they persist. Oliver and I see one element of our work as being able to work with people with such concerns to bring their research to a broader audience.
Working together, geographer and designer, we help scientists find and show narratives in their data. Perhaps counterintuitively, this is mostly an exercise in data reduction. We are both fond of saying that a large part of our job is cleaning hairballs. Take “The Seals Who Map the Southern Ocean.” This map started out as a mass of lines showing the routes traveled by hundreds of seals around Antarctica. The initial impact came from the sheer volume of tracks collected. Beyond that, we realized the tangle did not communicate much else. Our solution was to extract the journey of a single seal named Rudolf and add annotations and contextual detail such as bathymetry and ocean temperature to help readers grasp what a seal can teach us about warming seas.
This is the storytelling power of Small Data. Think of a nature documentary. You never hear about every gannet in the colony. The power comes from focusing on a breeding pair that represents the broader story. Barry Lopez’s story would’ve been insufferable if instead of hearing about the life of Corlis Benefideo we had to read the biographies of every person in Fargo. We find story in specificity. But specificity takes more work. More reporting, more analysis, more time.
In “The Mappist,” that sort of time-intensive mapping is what Benefideo longed for, what his employers loathed, and what drove him to strike out on his own to make 1,651 hand-colored maps of North Dakota. Oliver and I share a reverence for such historical methods. In fact, many “historical” maps can hold their own with the most data-driven of outputs of today.
Working together, geographer and designer, we’re able to combine cartographic details and typographic hallmarks of the past with the computer power of today. Charles Booth’s door-to-door “poverty map” of London remains among the most detailed social surveys ever undertaken. In The Information Capital, we used transparencies (as Benefideo might have done) to overlay Booth’s 1889 map with current levels of deprivation.
Working together, geographer and designer, we’re also able to imagine new roles for basemaps. Thanks to the range of Earth observation data now available, basemaps no longer have to be just backgrounds; they can become part of the story. For example, we can obtain meteorological data that reveals the winds encountered by albatrosses encircling Antarctica or satellite imagery that captures the ice floes used by snowy owls while hunting waterfowl. In fact, most of the data acquired for the Animals book was for the basemaps—over 200 gigabytes of terrain and vector layers, much of which did not make it to print. As we’ve learned, that’s just part of the process.
With each map, with each book, you’d think the process would get easier. In some ways it has. What once took us a week, we can now complete in under an hour. Part of this is thanks to new software, part of this is thanks to experience, but most is due to the fluency we have developed working together.
Oliver: Aspiring authors are often told to “write the book you want to read.” For cartographers, the advice could be, “make the map you want to frame.” As James and I were working on London: The Information Capital, we began to imagine a new type of book, one that we had always wanted but had never seen. Cartography books tended to fall into a few categories. There were instructional books that advised how to pick the appropriate projection or visualization technique. There were anthologies that gathered examples from the past or best practices from the present. There were gift books full of maps as jokes or one-off concepts. And there were, of course, atlases.
What we hadn’t seen were collections of original maps designed to comment on the world we live in now. “What if,” we wondered. What if we took a step back and used maps to reflect upon larger patterns? To us, the distinction was like the difference between a news article and an essay. We need both. And in writing and photography, we get both. A previous recipient of this award, Rebecca Solnit, builds each of her essay collections as an ensemble. Top photojournalists build a point of view through photo essays not single shots. Where, we wondered, was an equivalent for cartography?
With London: The Information Capital and Where the Animals Go, we aimed to build a case througha series of “data essays.” In London, it was a case for what data can reveal about life in a twenty-first century city. In Animals, what data can now tell us about the natural world. In both, we tried to take the long view. To put the present in the context of history while also looking to the future.
James and I are now at work on a new book. We’ve gathered data, designed spreads, written stories. But we’re still going back and forth on the book’s title because one of the nagging questions that we continue to revisit throughout our process is: what are we trying to say? What’s our point of view? What is the sum of 100 maps?
Originally, our publisher commissioned our London book under the working title: London Infographics. In a way, that’s a perfect example of how many editorial desks think of maps and graphics—as singular things about a subject: a map of London; a chart of population over time; or, as the narrator of “The Mappist” modestly described his own achievements, “some illustrations, however well done.”
After James and I had produced a few dozen such “illustrations,” we realized that the whole was communicating something more than its parts. We realized that this collection of data, pulled from public data stores and open data policies at the national and city level, would not be possible in any other city at that time. So the sum of 100 maps became not London Infographics but London: The Information Capital. The maps themselves were making an argument for open data, for London as an example for the rest of the world, for the power of maps—and mapmakers—to help inform policy and to improve our lives.
Flip through a magazine or scroll through a site and you’ll often see the map as the accompaniment, a complementary figure that helps elucidate “the main story.” It is our firm belief that maps can be the main story. We believe that you, the cartographer, can be the lead. You have a voice on par with writers and photographers. You have the power to make patterns visible. You have the power to show the change we see in this world. You have the power to warn, to reveal, to defend, to delight, to connect, to direct, to focus, to fascinate. You have the power to fire the imagination of a new generation, just like The City of Ascensions did for Phillip Trevino in “The Mappist,” just like those wooden puzzles and Casio Pathfinders did for us decades ago.
At the end of “The Mappist,” Corlis Benefideo says, “the real question, now, is what will you do?”
It’s a good one. A few suggestions:
To power your imagination, trust your instincts and keep the well full. At conferences, go to sessions on subjects you know nothing about.
To take your maps to the next level, find a collaborator.
And to find your voice, take a cue from Mr. Benefideo himself. You don’t have to retreat to North Dakota (or Michigan), but allow yourself time to push an idea as far as it’ll go. If there’s a subject you’re passionate about, don’t stop with a single map. Make a lot of maps. Maybe not 1,651, but enough to hear what they’re starting to say.
Because you, the mappists, have a voice. And we want to hear it.
I often liken the process of creating data visualizations to a game of snakes and ladders – you can race up the board with a great dataset only to land on a tricky issue with software that slides you backwards. And there’s always the longest snake lurking a few turns from the end – the realization that the map or graph just isn’t working for the audience and you will need to return to square one.
When starting out it can feel like there are many more snakes than ladders and this sense of frustration can be compounded by the fact that we only really share the successes – or at least the maps and graphics that made it out the door. But behind every success will be graphics that failed.
As I scroll past the many amazing maps and graphics I see online I need to remind myself it can be rather like aspiring to the ‘perfect’ lives of Instagram influencers without zooming out to realize what’s really happening behind the camera.
In recent weeks I have had a few people ask about challenges I’ve faced or to give examples of graphics that didn’t work out, so I thought this would be a good excuse to share some outtakes from my work on Atlas of the Invisible.
What made it into the book – both the text and the graphics – really is just the tip of an iceberg created from late nights shouting at crashed software, long days staring blankly at a spreadsheet or hours spent on drafts that were later pushed aside!
Go with the flow
When I was looking for some examples to feature in this post I happened upon this list of files – I recall they were the images I’d created of glacial flow lines after a tough day of coding.
‘BOOM’ is my file naming convention of choice when I think I have decisively solved a problem…it turns out that I needed a further 8 attempts and by looking at the time stamps another 7 hours to get to the final version!
This was just the flow lines for this map, there were many hours of additional layering and design to follow.
As it happens I also discovered the folder also contained probably one of the most beautiful outtakes – a map of the ice flows in Antarctica.
I think it works nicely as a standalone image, but it didn’t make the cut because we wanted to feature both Greenland and the Juneau Icefield as they told us the stories we were most interested in telling about accelerating ice in the face of climate change.
Too much shipping
Oftentimes you need to put significant effort into a preliminary idea to be able to decisively discard it as something that won’t work out. The image below is of shipping traffic around Denmark and it was created from an ENORMOUS dataset that took an age to download and format in order to map.
For whatever reason I just wasn’t feeling it – I didn’t know enough detail about the shipping lanes and boat behaviours to create the compelling narratives we aspire to. The data needed some cleaning up, which I wasn’t sure how best to do and we already had maps in the book that covered shipping routes, shipping’s impacts on the weather and also fishing. If I could have mastered a stronger story for this one it would have made it into the book at the expense of another on a nautical theme (we wanted a variety of topics). The draft made us question our choices again and feel firmer in our decision about what to include – and what not to.
Never a walk in the park
Of course, there are many more examples, but this final one eclipses them all as an example of when it’s sometimes best to let something go…hard though it is.
The map above is pretty much finished and it shows a long walk, short cycle, bus ride and finally train ride I did around London on a hot August day in 2018. The idea behind it was to demonstrate the various ways that fitness apps and sensors in your phone can track behaviour and be used to determine the modes of transport someone might be using. We triangulated these with images I took along the way.
So this is an afternoon puffing around London to collect the data, plus then a couple of days of processing it and creating the final map. It also felt quite personal to me and it told a bit of a story about parts of the city I enjoy visiting etc. We wanted it to be a relatable example of what our data can reveal, the privacy implications of such technology, and so on.
The initial reaction from our editor was that rather than being relatable the map was a bit random – why is my walk a story worth telling?
We then remembered that the US Military had doxed themselves with a fitness app…which even I had to admit was a much more interesting and compelling way of sharing the perils of tracking data, so my walk was cut. I consoled myself that at least it wasn’t raining that August afternoon…
As England emerged from its second national lockdown in early December, Boris Johnson, the UK prime minister, faced an onslaught of questions from MPs on both sides of the House of Commons. Each demanded clarity on what the arrangements would be for their particular constituency under the multi-layered tiers that would impose different COVID-19 restrictions on different areas.
They saw an ad-hoc logic behind the system outlined in the bill they were being asked to vote into law. In some cases – such as in Kent – restrictions were too general. In others – such as Slough – they were too specific.
Johnson responded by saying future restrictions would be “as granular as possible … to reflect … the human geography of the epidemic”. In theory, a more localised tiered approach is exactly what is needed once national infection rates come under control. It rekindles the “whack-a-mole” strategy for the flare-ups Johnson referred to earlier in the year. In reality, however, the government – like the rest of us – is looking increasingly confused by the complicated geographical units used to govern and map the country.
It could opt for obscure statistical units that best capture local outbreaks but that few people understand, or choose from a long menu of options used by local or national government. There’s something of a pick ‘n’ mix strategy at present that betrays how the UK’s geographic units were designed by different bodies, with little coordination, for a whole range of conflicting purposes – none of which were managing a pandemic. The result is a confusion of seemingly conflicting messages across government communications.
This is not helped by the fact that maps based on the same data produce very different pictures of the crisis if you split up the country differently. Depending on the size of the population of the area, you can come out with an infection rate as low as 295 per 100,000 people or as high as 736 per 100,000.
For this reason, scientists tracking the spread of the virus prefer to use units that encompass roughly the same number of people, which are geographies developed for the census (so called “output areas”). This approach has several advantages. COVID-19 hotspots can be linked to other contextual data, for example, such as on the ethnic makeup or the deprivation of an area.
But these units are not how the country is governed. For that, England is divided into constituencies and counties and “combined authorities” – to name just a few of the different units of governance. Map COVID-19 rates across these boundaries and you will get even more different infection rates, since a constituency can include a densely packed town and a sparsely populated rural area, for example. It’s an impossible problem to solve, but it can be managed through consistent policies and geography.
This is important because, as we’ve seen, local councils, MPs and metro mayors want to negotiate their own lockdown terms. Many combined authorities (city regions) are bristling at being treated as similar even if they are experiencing significantly varying disease patterns at local levels.
In London, many are questioning the rationale for treating the entire capital the same and cracks are appearing in the one-size-fits-all approach. Greenwich council, for example, entered into a heated argument with central government over its unilateral decision to close schools.
These disagreements show what happens when there is confusion about how data on infections should be interpreted. And when local, regional and national governments can’t agree, the public becomes confused too. That reduces compliance with the rules and ultimately allows the virus to spread more rapidly.
The law that England’s tiered restrictions are based upon has done little to simplify things. It previously listed the geography of counties and unitary authorities, but the public communication included the larger and more regional geography of combined authorities. The most recent legal amendments that have placed Greater London, and parts of Essex and Hertfordshire into Tier 3 are, in some cases, being set at a different geography again. The likes of Rochford District Council now make the list, for example, rather than being included in the broader Essex County Council as it was previously.
If more localised restrictions are to have a fighting chance of success, they need to do a better job of reflecting this complex and conflicting geography, even if only to give a clearer picture of how COVID-19 is spreading. The government would then be able to better communicate why particular restrictions are necessary to help control the pandemic. If people are told clearly why, and where, restrictions are being applied, they are much more likely to comply – potentially saving their own lives and the lives of others.
Really excited to announce that Atlas of the Invisible, the third book I have co-authored with Oliver Uberti will be in bookshops soon! You can pre-order it here!
“If you can’t convince them, confuse them.” If you watched the UK government’s COVID-19 briefing to announce and England-wide lockdown, you might have been reminded of this quote by Harry S Truman. Following slide after slide of maps and charts, there was growing frustration about the way nationally important statistics were being presented to the public.
Getting these things right is important. We’ve seen previously and in this pandemic that trust in government influences whether people follow public health guidelines. And in a UK survey earlier this year, those who had low levels of trust in the government’s ability to handle the outbreak were twice as likely to think its response had been confused and inconsistent. While a set of confusing slides won’t alone dictate how people behave, these things add up.
We don’t need high production values, or even much polish – it’s nice to feel like we’re seeing the latest data rather than something endlessly adjusted – but being comprehensible and looking professional will help support the message. At the moment, these slide decks are reminiscent of rushed conference presentations pieced together while the previous presenter was speaking. Here’s how to fix that.
Explain your working
Perhaps the biggest betrayal to an audience eager to understand is the phrase “as you can see”. It’s repeated many times at these briefings, and it’s too quickly followed by “next slide please”. The information shown is complex and takes a moment to digest. The presenters – the UK government’s chief medical adviser Chris Whitty and its chief scientific adviser Sir Patrick Vallance – need to slow down.
On the map below from briefing in question, how many of us noticed that the weekly case rates per 100,000 people didn’t increase by the same amount each time in the key? We had intervals of 25 for the first two categories, but then jumps of 50 until 200+. The map’s design also failed to show that the rate far exceeded 200 cases per 100,000 people in some areas. Wigan, for example, had 622 cases per 100,000 people.
One goal of a map maker is to reveal patterns that may exist in the data, and colouring is key to this – they have to decide when to move from one colour to another. In some cases it’s preferable to split up a narrow part of the distribution into lots of colours and then assign the rest to a few. Or you might assign each part of the distribution equally. Either is fine, but it needs to be explained, or else it’s a nuance that will get missed or misinterpreted.
The choice made for this map overemphasises small leaps in small numbers at the expense of big leaps in large numbers. Unless the values up to 25 and those between 25 and 50 had significance in policy, they could have been lumped into 0-50. Likewise, the map suggests anything greater than 200 doesn’t really matter – that a rate of 201 deserves the same colour as a rate of 601. This doesn’t seem right to me. But the point is, this system needs to be explained, because choosing different intervals can create a very different impression.
Consider the following graphs. The top left is the same as the government’s purple one above, whereas the others present exactly the same data, just with different sized intervals.
On this point as well, the presenters made their lives hard by using national maps when most of the action is in cities. These are hard to see at this scale. The maps pulled out London, but should have done the same for other urban areas.
Presentation matters too
On this map, we were supposed to focus on the dark brown areas – these are bad news. But instead our eyes can’t resist the greens. Whitty had to tell us that the brown areas were what we should be looking at.
Many people online also complained that the slides didn’t fit the screen. This was an error seen on the BBC only, which had set them up wrong, and wasn’t the government’s fault. However, it does suggest the government isn’t considering what devices people will use to view the press conferences. They appear to be designing for the 50-inch television they are viewing and not for the many people streaming or catching up on their phones.
It’s always a risky strategy to push content right to the edge of slides, as things can get cut off. The layout also failed to account for the chyrons that appear at the bottom of news broadcasts, which could easily have been anticipated and designed for.
Try and keep it simple
“This is a complicated slide,” said Sir Patrick Vallance as he drew things to a close, forgiving us for not fully understanding it. But this slide was crucial. It was the climax to the case for lockdown. The 16 maps and graphics that came before were just preamble. The two graphs on this slide told us that the NHS would likely run out of capacity to treat the sickest patients in only a few weeks if we didn’t act. It was all he needed to show.
Unfortunately, the dates were misaligned on both graphs (the one on the right takes us to the end of the year, the left mid-December). It’s splitting hairs perhaps, but it demonstrates again that no one took a breather to dot the Is and cross the Ts.
The abundance of acronyms and specialist language is also symptomatic of trying to throw too much at a general audience to build credibility through complexity. This approach risks alienating the audience – when actually there was one key message on Saturday: without lockdown we’ll run out of hospital beds within a few weeks and people we could otherwise save will die.
I want to be clear that I have tremendous respect for the teams of people involved in creating these maps and graphics. I also have sympathy with the scientific advisers themselves, who are treading the increasingly strained tightrope between science and politics. The fact that they are showing such a rich array of data in some quite interesting ways is a really good thing, and we need more of it.
But data visualisation and communication is different to epidemiological modelling. It’s hard to do well, even harder under pressure, though it is possible. Unfortunately, if the government briefings are anything to go by, it remains an overlooked and undervalued skill.
Like many people, the first graph I ever saw explaining climate change was in a school geography textbook. It showed the “hockey stick” curve of the Earth’s surface temperature over time, which has become one of the world’s most recognisable line graphs.
Despite relatively minor fluctuations, the line on the graph depicting global surface temperature remains almost horizontal across centuries, before suddenly inclining to an almost vertical trajectory over the past 50 years. Since 1970 the rate of global temperature increase has hit an unprecedented 1.7°C per century.
One challenge of understanding the information contained in this hockey stick graph – and this is a gift to climate-change deniers – is the inclusion of the grey fuzz of “uncertainty data”: outlying data points that can be cherry-picked to raise doubts about the mass of evidence supporting a general warming trend.
Global surface warming: the hockey stick
Uncertainty is a complex thing to communicate in a single chart. In 2018 the UK-based climate scientist Ed Hawkins chose to omit it altogether when he presented his “warming stripes” graphic to help clearly visualise key trends in climate data. Hawkins explained that the warming stripes were designed to remove all superfluous information, leaving behind only the undeniable scientific evidence of a steadily warming world.
Climate change in warming stripes
If getting to grips with all the data and complexity in the hockey stick required a long read, Hawkins’ climate stripes give us the headline. The stripes are now a global phenomenon, having appeared on the lapels of US senators, the ties of TV weather presenters and on the front cover of The Economist.
As calls for change grow louder in light of the latest IPCC (Intergovernmental Panel on Climate Change) report and in the run up to COP26 conference in Glasgow this November, it’s time to focus on how data visualisation can help people grasp the challenges that lie ahead.
The power of maps
One misconception about the climate crisis is that warming will be uniform across the world. Deniers cite cold fronts or blizzards as evidence that warming is exaggerated, or hark back to past heatwaves – such as that experienced by the UK in 1976 when temperatures exceeded 35°C – as proof that the scientists have got it wrong.
Apart from this misleading conflation of weather (daily conditions) and climate (long-term conditions), this kind of argument misses the complex patchwork of effects that interact to create what gets reported in the headline figures.
Maps can be an invaluable weapon against this misunderstanding. For the first time, the IPCC has released an “interactive atlas” with its latest report, allowing audiences to pan and zoom through the data themselves. But if you give the IPCC’s atlas a try, you can see how it’s hard to capture complexity for a specialist audience while retaining simplicity for a global audience.
Most users are unlikely to closely engage with towering datasets named ‘CMIP5’ or ‘APHRODITE’, or with the mass of code that constitutes the IPCC-WG1 repository on Github. Although it’s a step in the right direction, what is needed are more universally accessible visualisations that are able to show where we’re heading in no uncertain terms.
With that in mind, when I set out to map global warming for a new book entitled Atlas of the Invisible, my co-author Oliver Uberti and I chose to combine the most important lessons from the warming stripes with the intricacies of geographical context.
This intriguingly named “Peirce quincuncial” projection, which you can see below, is a type of 2D map that flattens the Earth into a grid of 130 mini maps called tiles. Like all projections, it’s not a perfect representation of the 3D Earth, since some areas are stretched more than others. But it lets us create a series of tiles representing the planet in each year from 1890 to 2019, coloured by how and where temperatures deviated from a reliable baseline measured between 1961 and 1990. Blue areas represent temperature anomalies between -2°C and 0°C, while red areas represent anomalies between 0°C and 3°C and grey represents insufficient data.
Heat gradient map
Reading the images from left to right reveals that while heatwaves and cold spells speckle the grid, tiles representing the current century are increasingly filled with warm tones. For example, compare the few pink splotches in 1976 when the UK experienced its famous heatwave to years later in 2006 and 2016 when ruddy hues spanned the globe. In fact, the ten hottest years on record have occurred since 2005.
Heat gradient map: specific years
Time to think local
When mitigation targets aim to keep the overall global temperature increase at an average of below 1.5 or 2°C, we need data visualisations to remind us that there can still be large local variations even when such targets are achieved, with the warming creating drastic and often devastating conditions for those living in affected areas.
Generalised warming will inevitably affect some places far worse than others, causing knock-on effects like sea-level rises and storms in different areas. For proof, look to the 2021 summer heatwave experienced by many parts of Europe yet escaped by the UK, the “heat dome” that scorched British Columbia in June, or the Arctic, where temperatures are rising at twice the global rate.
Even within cities, conditions can vary from neighbourhood to neighbourhood. Across the US, global warming is compounding the legacy of racist housing policies enacted through a process known as redlining. This rated the “investment risk” of urban areas, condemning many black neighbourhoods to a “hazardous” rating and thus to reduced infrastructure and increased poverty.
As the New York Times has expertly mapped, such areas saw a lack of investment in – amongst other things – green spaces and street trees. This has resulted in some historically redlined neighbourhoods suffering summers that are up to 7°C warmer compared to their non-redlined counterparts.
Maps reveal these social injustices in the UK, too. Local authorities are under pressure to turn a blind eye to flood-risk maps in order to permit thousands of “affordable” homes to be built for those priced out of higher ground.
The power of maps lies in their ability to show us simultaneously that as global average temperatures rise, local conditions threaten to become ever more extreme. We now need to better harness that power to inspire action.
The maps above were created for an article in The Conversation entitled Next slide please: data visualisation expert on what’s wrong with the UK government’s coronavirus charts. In it I argue that there needs to be better data visualisations in government briefings. I give a particular example about how maps can appear differently depending on the choice of colours – in this case case rates of COVID-19 in the week up to the 30th October. The maps were designed to be created in R and I have posted the code below.
#Packages library(tmap) library(rgdal) library(sp) library(spatialEco) # load in file input<- readOGR(dsn="http://www2.geog.ucl.ac.uk/~ucfa012/COVID_Oct30_Rate.GeoJSON", layer="COVID_Oct30_Rate") # Remove the Nas (this is Scotland and Wales and a function from the SpatialEco package) input<- sp.na.omit(input, col.name="LA_Cln") #Government choice - extra colours for low values opt1<-tm_shape(input) + tm_fill("Rate_num", palette = "Reds", legend.hist = T, breaks=c(0,25,50,100,150,200,736.2), title="COVID-19 Cases\nPer 100k (Weekly)") + tm_borders(col="black", lwd=0.1) #Suggestion 2 - same colour breaks up until 200+ plus cases opt2<-tm_shape(input) + tm_fill("Rate_num", palette = "Reds", legend.hist = T, breaks=c(0,50,100,150,200,736.2),title="COVID-19 Cases\nPer 100k (Weekly)") + tm_borders(col="black", lwd=0.1) #Suggestion 3 - consistent breaks up to 500+ cases opt3<-tm_shape(input) + tm_fill("Rate_num", palette = "Reds", legend.hist = T, breaks=c(0,100,200,300, 400, 500,736.2),title="COVID-19 Cases\nPer 100k (Weekly)") + tm_borders(col="black", lwd=0.1) #Suggestion 4 - natural breaks in the distribution using the Jenks algorithm opt4<-tm_shape(input) + tm_fill("Rate_num", palette = "Reds", legend.hist = T, style="jenks",title="COVID-19 Cases\nPer 100k (Weekly)") + tm_borders(col="black", lwd=0.1) #Suggestion 5 - equal numbers of local authorities in each colour band opt5<-tm_shape(input) + tm_fill("Rate_num", palette = "Reds", legend.hist = T, style="quantile",title="COVID-19 Cases\nPer 100k (Weekly)") + tm_borders(col="black", lwd=0.1) #Suggestion 6 - extra colours for higher values opt6<-tm_shape(input) + tm_fill("Rate_num", palette = "Reds", legend.hist = T, breaks=c(100,200,300,400,500,550,600,650,700,736.2),title="COVID-19 Cases\nPer 100k (Weekly)") + tm_borders(col="black", lwd=0.1) #uncomment below if you want to save as PDF. I have only selected 4 of the above options. #pdf("map.pdf",width=20, height=5) tmap_arrange(opt1,opt2,opt6,opt4, ncol=2) #dev.off() #Voila!
Here is a short video I did for the Royal Geographical Society to show how new satellite data can reveal the daily changes to nitrogen dioxide (and other pollutants) in the atmosphere in unprecedented detail. For a longer version of the video and further explanation see here.
Here’s a video I filmed with the Financial Times to help explain the importance of gerrymandering to the outcomes of elections.