kottke.org posts about infoviz
National Geographic Infographics is an anthology published by Taschen of some of the best infographics featured by National Geographic in the past 128 years.
Through seven sections — History, The Planet, Being Human, Animal World, World of Plants, Science and Technology, and Space — we encounter the rise and fall of the Roman Empire, the mysterious origins of the Easter Island statues, Cleopatra’s Alexandria and a history of Hawaiian surfboarding, all distilled in expert, accessible graphic form. We discover how our genetic patterns have been pieced together over the years or how hip-hop emerged as a cultural heavyweight; we get to grips with global warming, and explore our ever-expanding study of an ever-expanding universe.
It’s that time of year again. No, not Christmas or Hanukkah. As the year winds down, it’s an opportunity for Americans to investigate how differently they use words in different parts of the country. In December 2013, for example, people lost their damn minds over the NY Times’ dialect quiz. This year, you can play around with The Great American Word Mapper which uses Twitter data from 2014 to plot geographic usage patterns.
For instance, you can see where people use “supper” vs. “dinner” (see above). The map indicates mixed usage where I grew up, which checks out…we mostly said “supper” but “dinner” was not uncommon, particularly as I got older. Other results are less useful…the Twitter-based “soda” vs. “coke” vs. “pop” doesn’t tell you as much as directly asking people what they call soft drinks.
The swearing maps are always fun (see also the United States of Swearing)…I wonder why “shit” is so relatively popular in the South?
Some other interesting searches: “moma” (alternate spelling of “momma” in the South with a small pocket of usage around NYC for MoMA), “city” doesn’t give the result you might expect, the distribution of “nigger” vs “nigga” suggests they are two different words with two different meanings, and in trying to find a search that would isolate just urban areas, the best I could come up with was “kanye” (or maybe “cocktails” or “traffic”). And harsh, map! Geez. (via @fromedome)
This is cool and a little mesmerizing: animated US maps showing the most popular baby name in each state from 1910 to 2014 for boys and girls. There are three separate visualizations. The first just shows the most popular baby name in each state. Watch as one dominant name takes over for another in just a couple of years…the Mary to Lisa to Jennifer transition in the 60s and 70s is like watching an epidemic spread. Celebrity names pop up and disappear, like Betty (after Betty Boop and Betty Grable?) and Shirley (after Shirley Temple) in the 30s. The boy’s names change a lot less until you start getting into the Brandons, Austins, and Tylers of the 90s.
The next visualization shows the most particularly popular name for each state, e.g. Brandy was the most Louisianan name for female newborns in 1975. And the third visualization shows each name plotted in the averaged geographical location of births — so you can see, for example, the northward migration of Amanda during the 80s.
P.S. Guess what the most popular boy’s name in the state of my birth was the year I was born? And the most particularly popular boy’s name in the state I moved to just a year later? Jason. I am basic af.
Update: From Flowing Data, some graphs of the most unisex names in US history. (thx, paul)
The New York Times took a map of the US and split it in two based on areas that voted for Clinton and Trump in the 2016 election. (Clinton’s map is pictured above.)
Mrs. Clinton’s island nation has large atolls and small island chains with liberal cores, like college towns, Native American reservations and areas with black and Hispanic majorities. While the land area is small, the residents here voted for Mrs. Clinton in large enough numbers to make her the winner of the overall popular vote.
That’s fun, but it’s another reminder of how strictly geographical maps distort election results.
P.S. They missed a real opportunity to call the chain of islands in the southern states The Cretaceous Atoll.
Neil Halloran, creator of the excellent Fallen of World War II interactive visualization of the casualties of WWII, is working on a similar visualization about the possible effects of a global nuclear conflict. He recently uploaded an in-progress video of the project with a special 2016 election message at the end. Amazing and scary to see how much of a difference WWII made in the global death rate and how minuscule that would be in comparison to a global nuclear war.
The tire tracks in this parking lot make a tree pattern in the snow, a self-producing infographic of the cars’ collective pathway to their parking spaces. It’s fun to trace individual tracks — I’m fascinated by the one that comes in, starts right, turns back to the left, then heads back down before turning toward the left again into a space.
The photo was taken in a Shell Centre parking lot near Waterloo Station in 1963. Photographer unknown. (via @robnitm)
Update: Nicholas Felton shared an annotated single-car version of a car’s tracks in the snow.
Update: A reader randomly picked up a copy of a book recently called The World From Above, “a pretty brilliant collection of aerial photographs, mostly black and white, published in the mid 60’s” and the parking lot photo was in it. No photographer listed, but the photo is credited to dpa, the German Press Agency. (thx, david)
The United Kingdom, Spain, Denmark, Sweden, and six other European countries still have hereditary royal leaders and they are all related to each other. Royal Constellations is an interactive infographic for exploring the ancestral relationships of Europe’s royalty.
Royal & aristocratic families are known for their fondness of marrying within their own clique. Restraining aggression between two families, creating a stronger front towards a third family, increasing territorial acquisitions, legal claim to a foreign throne through inheritance are some of the most common reasons.
This leads to very interesting & entangled family trees which the visual below tries to convey. It shows how all 10 of the current hereditary royal leaders of Europe can be connected to each other through their ancestors. We don’t have to look very far back. Even the most distant royal relatives have their shared forebears born after the year 1700.
This is a lovely infographic from Eleanor Lutz of a bunch of different heartbeat EKG waves, from a normal heartbeat to a flatline to ventricular fibrillation (“must be treated immediately with CPR and defibrillation”.) Prints are available.
Mike Kelley has travelled to airports all over the world, photographing planes taking off and landing and then stitching them together into photos showing each airport’s traffic. (via @feltron whose book features an Airportrait on the cover)
The Information is Beautiful Awards have announced the shortlist of nominees for the best infographics, data visualizations, and data journalism for 2016. Literally hours of exploration here. Some well-deserved shouts out to Polygraph (multiple projects, including their breakdown of film dialogue by gender and age), Nicholas Felton’s Photoviz, climate spirals, FiveThirtyEight’s 2016 election forecast map, and many other projects you might have seen here or elsewhere.
The images above are from Adventures in Mapping, Polygraph, and Shipmap.
OneZoom is an interactive zoomable map of “the evolutionary relationships between the species on our planet”, aka tree of life. Browsing around is fun, but you’ll want to use the search function to find specific groups and animals, like mammals, humans, and mushrooms. The scale of this is amazing…there are dozens of levels of zoom. (via @pomeranian99)
From XKCD, a typically fine illustration of climate change since the last ice age ~20,000 years ago.
When people say “the climate has changed before”, these are the kinds of changes they’re talking about.
And then in the alt text on the image:
[After setting your car on fire] Listen, your car’s temperature has changed before.
The chart is a perfect use of scale to illustrate a point about what the data actually shows. Tufte would be proud.
Update: Tufte is proud. (via @pixelcult)
In today’s installment of terrifying graphics about climate change, the NY Times made a series of three maps showing the potential rise of 100 degree temperatures across the United States if current greenhouse gas emission trends continue through the end of this century. Look at the areas in orange and red on the 1991-2010 map: what sort of landscape do you picture? Keeping that landscape picture in your mind, look at the orange and red areas on the 2060 and 2100 maps. Yep! And Phoenix with 163 days above 100 degrees — that’s every day from March 25th to September 4th over 100 degrees.
P.S. A word about climate change and rising temperatures. The temperature that climate scientists typically reference and care about with regard to climate change is “the average global temperature across land and ocean surface areas”. According to the NOAA, the average temperature of the Earth in the 20th century was 13.9°C (57.0°F). In 2015, the average global temperature was 0.90°C (1.62°F) above that.
In order to avoid dangerous effects of climate change, climate scientists advocate keeping the global average temperature increase below 2 degrees (and more recently, below 1.5 degrees). In late 2015, 195 nations came together in Paris and agreed to:
[Hold] the increase in the global average temperature to well below 2°C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5°C above pre-industrial levels, recognizing that this would significantly reduce the risks and impacts of climate change
That’s degrees Celsius, not Fahrenheit. I don’t know about you, but as an American, when I hear 2 degrees, I think, oh, that’s not bad. But 2°C is an increase of 3.6°F, which does seem significant.
Note also that it specifies keeping the temperature “below pre-industrial levels” and not below 20th century levels. It is maddeningly difficult to track down an exact figure for the pre-industrial global temperature, partially because of a lack of precise data, partially because of politics, and partially because of the impenetrability of scientific writing. From a piece Eric Holthaus wrote for FiveThirtyEight earlier this year:
It sounds easy enough to measure global warming: see how hot it was, compare it to how hot it used to be. But climate scientists have several ways of measuring how hot it used to be. NASA’s base period, as I mentioned above, is an average of 1951-80 global temperatures, mostly because that was the most recently available 30-year period when the data set was first created. By chance, it’s also pretty representative of the world’s 20th-century climate and can help us understand how much warmer the world has become while many of us have been alive.
Other organizations go further back. The Intergovernmental Panel on Climate Change, the body of climate scientists that was formed to provide assessments to the United Nations, bases its temperature calculations on an 1850-1900 global average. There was about 0.4 degrees of warming between that time period and the NASA base period.
Climate scientists often refer to that 1850-1900 timespan as “pre-industrial” because we don’t have comprehensive temperature data from the 1700s. But meteorologist Michael Mann, director of Penn State University’s Earth System Science Center, has argued that an additional 0.25 degrees of warming occurred between the start of the Industrial Revolution (around 1750) and 1850. Including Mann’s adjustment would bring February 2016 global temperatures at or very near 2 degrees above the “pre-industrial” average.
I now completely understand why some people deny that anthropogenic climate change is happening. Seriously. I looked for more than 30 minutes for a report or scientific paper that stated the average global temperature for 1850-1900 and I couldn’t find one. I looked at UN reports, NASA reports, reports from the UK: nothing. There were tons of references to temperatures relative to the 1850-1900 baseline, but no absolute temperatures were given. Now, I don’t mean to get all Feynman here, but this is bullshit. When the world got together in Paris and talked about a 1.5 degree increase, was everyone even talking about the same thing? You might begin to wonder what the scientists are hiding with their obfuscation.
Anyway, the important point is that according to climate scientists, we are already flirting with 1.5°C of global warming since pre-industrial times. Which means that without action, the spread of those Phoenician temperatures across the circa-2100 United States is a thing that’s going to happen.
The population of NYC is equal to the combined populations of Vermont, Alaska, New Mexico, North Dakota, South Dakota, Wyoming, Montana, and West Virginia. Here’s what that looks like on a map.
Put another way: 16 US Senators represent as many people in those states as a fraction of one of New York States’ Senators represent the population of NYC. A Senator from Wyoming represents 290,000 people while one from New York represents 9.8 million people…and in California, there are 19 million people per Senator. That gives a Wyoming resident 65 times the voting power of a California resident.
Oh, this new book from Jennifer Daniel and New Scientist looks great: The Origin of (almost) Everything.
Together they take us on a whistle-stop tour from the start of our universe (through the history of stars, galaxies, meteorites, the Moon and dark energy) to our planet (through oceans and weather to oil) and life (through dinosaurs to emotions and sex) to civilization (from cities to alcohol and cooking), knowledge (from alphabets to alchemy) ending up with technology (computers to rocket science). Witty essays explore the concepts alongside enlightening infographics that zoom from how many people have ever lived to showing you how a left-wing brain differs from a right-wing one.
And Stephen Hawking wrote the foreword. You fancy, Jennifer Daniel!
From Clive Thompson, a history of the infographic, which was developed in part to help solve problems with an abundance of data available in the 19th century.
The idea of visualizing data is old: After all, that’s what a map is — a representation of geographic information — and we’ve had maps for about 8,000 years. But it was rare to graph anything other than geography. Only a few examples exist: Around the 11th century, a now-anonymous scribe created a chart of how the planets moved through the sky. By the 18th century, scientists were warming to the idea of arranging knowledge visually. The British polymath Joseph Priestley produced a “Chart of Biography,” plotting the lives of about 2,000 historical figures on a timeline. A picture, he argued, conveyed the information “with more exactness, and in much less time, than it [would take] by reading.”
Still, data visualization was rare because data was rare. That began to change rapidly in the early 19th century, because countries began to collect-and publish-reams of information about their weather, economic activity and population. “For the first time, you could deal with important social issues with hard facts, if you could find a way to analyze it,” says Michael Friendly, a professor of psychology at York University who studies the history of data visualization. “The age of data really began.”
Using the results of a recent report by a team of Yale researchers, this visualization shows the growth of urbanization across the globe from 3700 BC to the present day. There is an amazing flurry of activity in the last few seconds of the video because:
By 2030, 75 percent of the world’s population is expected to be living in cities. Today, about 54 percent of us do. In 1960, only 34 percent of the world lived in cities.
There are now 21 Chinese cities alone with a population of over 4 million.
From Flowing Data, an animated infographic that shows how the American diet has changed since 1970. We eat less beef, potatoes, margarine, and whole milk than we used to, but more chicken, cooking oil, bananas, and Italian cheese.
Betsy Mason and Greg Miller are writing a new blog for National Geographic about maps called All Over the Map. Here’s a mission statement.
There is something magical about maps. They transport you to a place you’ve never seen, from the ocean depths to the surface of another planet. Or a world that exists only in the imagination of a novelist.
Maps are time machines, too. They can take you into the past to see the world as people saw it centuries ago. Or they can show you a place you know intimately as it existed before you came along, or as it might look in the future. Always, they reveal something about the mind of the mapmaker. Every map has a story to tell.
You can also follow their progress on Twitter and Instagram. They recently shared this comparative rivers and mountains chart on Instagram; it’s one of my all-time favorite charts.
From Matt Daniels at Polygraph, a moving timeline of the 22,000 songs that hit the top 5 on the Billboard charts from 1958-2016. Whoa, there is a lot of pop music I missed in the late 90s through the late 2000s.
See also The most timeless songs of all time and Interactive timeline: listen to the #1 rap songs from 1989-2015.
The traveling salesman problem is a classic in computer science. It sounds deceptively easy: given any number of cities, determine the shortest path a traveling salesman would have to travel to visit them all. This video shows how the “obvious” solution — “well, just start somewhere and always visit the next closest town!” — doesn’t hold up well against other approaches. (via @coudal)
From Bill Rankin at Radical Cartography, a series of maps showing the rapid explosion of slavery in the United States from 1790-1860. Departing from previous efforts, Rankin used a uniform grid of dots to represent slave populations rather than counties.
First, I smash the visual tyranny of county boundaries by using a uniform grid of dots. The size of each dot shows the total population in each 250-sqmi cell, and the color shows the percent that were slaves. But just as important, I’ve also combined the usual county data with historical data for more than 150 cities and towns. Cities usually had fewer slaves, proportionally, than their surrounding counties, but this is invisible on standard maps.
A detail that struck me while cycling through the years was that the number of slaves as a percentage of the total population of the South stayed relatively steady at 33% from 1790 to 1860.
Eleanor Lutz’s latest infographic creation is a set of animated virus trading cards.
In 1989, a Rockwell engineer named Ron Jones published his Integrated Space Plan, a detailed outline of the next 100 years of human space travel, from continuing shuttle missions in the 1990s to the large scale habitation of Mars. The plan includes all sorts of futuristic and day-dreamy phrases like:
Create new moons for Mars if required
Humanity begins the transition from a terrestrial to a solar species
Humanity commands unlimited resources from the Moon and asteroids
Space drives global economy
Independent spacefaring human communities
Wired has a good look at how the plan came to be.
The graphic is divided into nine columns that show, in chronological order, the path toward human exploration of deep space. The center row of boxes, the “critical path,” outlines the major milestones Jones decided were attainable within the next century of space travel; the boxes to the left and right of the critical path are support elements that must be realized before anything on the critical path can happen. The Integrated Space Plan can be read top to bottom and left to right. The big circles intersecting the boxes are the the plan’s overarching long-range goals, which include things like Humanity begins the transition from a terrestrial to a solar species and Human expansion into the cosmos. In many ways, it’s a graphical to-do list.
The keen observer will note that we are waaaaay behind in the plan. A lunar outpost was supposed to be up and running before 2008 and a self-supporting lunar base is due to happen in the next year or two. Can Musk and Bezos get us back on track? (via @ftrain)
Nicholas Felton is out with a new book on information visualization and photography called Photoviz.
The stories told with graphics and infographics are now being visualized through photography. Fotoviz shows how these powerful images are depicting correlations, making the invisible visible, and revealing more detail than classic photojournalism.
Ahhhhh, this looks amazing. And is right up my alley as well…I quickly looked through some of the images featured in the book and I’ve posted many of them here before (see time merge media for instance). Can’t wait for this one to arrive.
A new print from Pop Chart Lab “traces the trajectories of every orbiter, lander, rover, flyby, and impactor to ever slip the surly bonds of Earth’s orbit and successfully complete its mission — a truly astronomical array of over 100 exploratory instruments in all.” Awesome. Basically, I am a sucker for things with curvy lines and planets.
From Pantheon at MIT, an adjustable graph of which kinds of people were globally famous in different eras. Up until the Renaissance, the most well-known people in the world were mostly politicians and religious figures, with some writers and philosophers thrown in for good measure:
Starting with the Renaissance through the beginnings of the Industrial Revolution, politicians, writers, painters, and composers become more prominent:
For the past 50 years, athletes and entertainers dominate the list, with footballers making up almost a third of the most known. (If you only go back to 1990, actors dominate.)
Politicians rate slightly behind tennis players (but ahead of pornographic actors) and religious figures are not represented in the graph at all.
If you’re curious about the data, you can read about their methodology and sources.
The product of a collaboration between Polygraph and Billboard, this interactive timeline lets you listen to the top rap song in the US from 1989 to 2015 as you see the single jockeying in the top 10.
Late last year, Todd Schneider did a big data analysis of taxi and Uber usage in NYC. This morning, he posted the results of a similar analysis for Citi Bike.
But unlike the taxi data, Citi Bike includes demographic information about its riders, namely gender, birth year, and subscriber status. At first glance that might not seem too revealing, but it turns out that it’s enough to uniquely identify many Citi Bike trips. If you know the following information about an individual Citi Bike trip:
1. The rider is an annual subscriber
2. Their gender
3. Their birth year
4. The station where they picked up a Citi Bike
5. The date and time they picked up the bike, rounded to the nearest hour
Then you can uniquely identify that individual trip 84% of the time! That means you can find out where and when the rider dropped off the bike, which might be sensitive information. Because men account for 77% of all subscriber trips, it’s even easier to uniquely identify rides by women: if we restrict to female riders, then 92% of trips can be uniquely identified.
Todd Schneider used a couple publicly available data sets (NYC taxis, Uber) to explore various aspects of how New Yorkers move about the city. Some of the findings include the rise of Uber:
Let’s add Uber into the mix. I live in Brooklyn, and although I sometimes take taxis, an anecdotal review of my credit card statements suggests that I take about four times as many Ubers as I do taxis. It turns out I’m not alone: between June 2014 and June 2015, the number of Uber pickups in Brooklyn grew by 525%! As of June 2015, the most recent data available when I wrote this, Uber accounts for more than twice as many pickups in Brooklyn compared to yellow taxis, and is rapidly approaching the popularity of green taxis.
…the plausibility of Die Hard III’s taxi ride to stop a subway bombing:
In Die Hard: With a Vengeance, John McClane (Willis) and Zeus Carver (Jackson) have to make it from 72nd and Broadway to the Wall Street 2/3 subway station during morning rush hour in less than 30 minutes, or else a bomb will go off. They commandeer a taxi, drive it frantically through Central Park, tailgate an ambulance, and just barely make it in time (of course the bomb goes off anyway…). Thanks to the TLC’s publicly available data, we can finally address audience concerns about the realism of this sequence.
…where “bridge and tunnel” folks go for fun in Manhattan:
The most popular destinations for B&T trips are in Murray Hill, the Meatpacking District, Chelsea, and Midtown.
…the growth of north Williamsburg nightlife:
…the privacy implications of releasing taxi data publicly:
For example, I don’t know who owns one of theses beautiful oceanfront homes on East Hampton’s exclusive Further Lane (exact address redacted to protect the innocent). But I do know the exact Brooklyn Heights location and time from which someone (not necessarily the owner) hailed a cab, rode 106.6 miles, and paid a $400 fare with a credit card, including a $110.50 tip.
as well as average travel times to the city’s airports, where investment bankers live, and how many people pay with cash vs. credit cards. Read the whole thing and if you want to play around with the data yourself, Schneider posted all of his scripts and knowhow on Github.
Update: Using summaries published by the New York City Taxi & Limousine Commission, Schneider takes a look at how taxi usage in NYC is shrinking and how usage of Uber is growing.
This graph will continue to update as the TLC releases additional data, but at the time I wrote this in April 2016, the most recent data shows yellow taxis provided 60,000 fewer trips per day in January 2016 compared to one year earlier, while Uber provided 70,000 more trips per day over the same time horizon.
Although the Uber data only begins in 2015, if we zoom out to 2010, it’s even more apparent that yellow taxis are losing market share.
Lyft began reporting data in April 2015, and expanded aggressively throughout that summer, reaching a peak of 19,000 trips per day in December 2015. Over the following 6 weeks, though, Lyft usage tumbled back down to 11,000 trips per day as of January 2016 — a decline of over 40%.