October 16, 2014

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Daily diets vary considerably around the world—and the food we eat often mirrors the wider structural circumstances of the places we live in. Whether influenced by strained foreign relations, growing economies, fluctuating market prices, or shifting environmental conditions, the food we consume depends on where we live. What the World Eats, our latest piece for National Geographic’s Future of Food series, compares national diets and consumption patterns across a variety of countries over the last 50 years.

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The caloric intake of the average person in 2011

The project breaks down the food items that fuel the daily diet of each country, and also shares a detailed view of national and per person meat intake. Adding the lens of meat consumption is important in that it sheds light on the larger agricultural, economic, and political systems in each nation. The project data was sourced from the Food and Agriculture Organization of the United Nations (FAO), which has collected a trove of global data on food production, consumption, trade, emissions, and other agricultural indicators.

We designed the information in two forms. The daily diets are represented by pie charts (or “donuts” as they’re now known around the office, and cited regularly to remind Terrence that he should bring in radial morning treats for the rest of us). The proportion of each food item (meat, dairy, produce, etc.) in the diet is represented by the amount of space it occupies in the circle. In developing countries, grains — which are often less expensive — make up a greater portion of the diet, whereas wealthier countries have more diverse breakdowns. Circle size reflects the average daily intake of calories or grams per person. Somalia, with the lowest per person calorie consumption in the world, has a chart that is half the area of the U.S. chart (where the average person consumes over twice the calories of the typical Somali).

In toggling between grams and calories, you can see that quantity of food consumption does not translate into caloric yields. For instance, over half of the typical Chinese diet is composed of produce, yet it accounts for only 15% of daily caloric intake.

The second section of the graphic, meat consumption, is composed of time series charts. Given the high cost and multitude of resources required to raise animals, national meat consumption is more susceptible than the overall diet to changing external circumstances.

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The Gulf War had a drastic impact on the availability of meat in Kuwait from 1990 to 1991.

Raising animals for meat consumption is taxing to both agricultural and financial resources. Livestock-based food production accounts for about 20% of global greenhouse gas emissions. Further, raising animals for food demands far more water, feed, and land than it would otherwise require to eat crops directly (note, a single cow requires a lifespan’s worth of resources, whereas using a space for crop production can yield foodstuffs annually). To bring Thomas Malthus into the discussion, we have a limited quantity of natural resources needed to feed an exponentially increasing population. The average person today eats twice as much meat than 50 years ago. Yet eating meat — especially livestock– is an inefficient means of feeding the earth’s fast-growing population.

Often as countries acquire more wealth, the proportion of grains in the diet declines, and individuals are better able to diversify the contents of their plates with more expensive animal products like meat and dairy. Additionally, impacts of war, tense foreign relations, and even widespread religious practices are visible through a country’s meat consumption.

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To this end the diet and meat consumption of more developed places like the U.K. have remained relatively unchanged, while the influx of China’s population and economy has led to unrivaled growth in both national and per person meat consumption.

Visit the site to explore the data, compare consumption across countries, and learn about the factors that influence the way people eat around the world.

October 13, 2014

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Just ran across this photo of Darcy Bowden, my high school “Production Art” teacher, during a brief visit to Fathom last summer. Her class was a two-hour studio that I was able to take both my junior and senior year—my first exposure to real graphic design exercises (creating black and white ink drawings of concepts like “contrast,” or making artifacts in the style of other eras of design, and so many others…) and gave me a chance to build a portfolio that helped me get into design school. I’d wanted to take the class ever since reading about it in the course catalog as an eighth grader picking out courses for my first year of high school.

Ms. B also kept Phillip Meggs’ History of Graphic Design (which at the time had a different—though still fairly atrocious—cover) checked out of the school library for the whole year, so I could read it from cover to cover. Such a great book, and perhaps a small thing, but huge for me to get that exposure as a seventeen-year-old. And she helped with the bigger things too—like introductions for internships and letters of recommendation for schools—but sometimes it’s the small things (whether the design exercises, a great group of people for class crits, or history books) that really stick with you.

So thanks to Ms. Bowden and the many other great mentors I’ve had over the years, and here’s to my friends who are teaching this fall and having the same kind of impact on their own students.

October 10, 2014

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Today, in collaboration with Sarah Rinaldi, we released a video documenting Open India, an interactive visualization we developed for the World Bank Group. The video was showcased at the Annual Meetings of the International Monetary Fund (IMF) and the World Bank Group (WBG). The Annual Meetings bring together central bankers, ministers of finance and development, private sector executives, and academics to discuss issues of global concern, including the world economic outlook, poverty eradication, economic development, and aid effectiveness.

We interviewed key stake holders associated with the World Bank Group‘s Country Partnership Strategy with India (CPS), which develops transformational solutions aimed at ending extreme poverty and promoting shared prosperity. The video frames the context around the app, and reflects the humanity behind the data. The interviews address why visualizing and interacting with the information improves the transparency, accountability, and opportunities for growth and progress with India’s CPS.

“This app is to generate ideas, People will drive India forward, and will drive the rest of the world forward with their aspirations, with their ideas, and with the incredible potential of this country.”
Onno Ruhl, India Country Director, World Bank

The video will help the status of India’s CPS reach a larger audience at the 2014 Annual Meetings of the IMF and WBG.

It was a pleasure working with talented Sarah Rinaldi in documenting the project, and we look forward to future collaborations.

October 09, 2014

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Last Wednesday afternoon we noticed a massive traffic surge on the Fathom website, with all visitors loading one specific image of the All Streets project from 2008. How random! A quick glance at the referring page data showed us the cause: someone had come across All Streets and shared it on Reddit’s dataisbeautiful subreddit. And it was blowing up on the front page.

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All Streets plots 240 million road segments in the United States.

For those unfamiliar with Reddit, it’s a massively popular site that aggregates online social and news material. People go there to share and comment on articles, pictures or videos. They share just about anything and everything they find interesting, thought-provoking, or funny. Whether you love Reddit, hate it, or simply find it a waste of time, the fact remains that when a link to a website lands on the front page, that site is going to receive an overwhelming amount of traffic that can bog down web servers or even take them offline—what some people call the Reddit Hug of Death.

As you can see from the below graph of our web traffic, we went from double-digit numbers of visitors per hour before the post, to a sustained peak of around 30,000 visitors per hour! Like most operations our size, this explosion in traffic caught us off-guard at first. Fortunately, we were able to do some fast AWS whispering and got back online in minutes. By the end of the rush, we had served over 250,000 visitors, and the project had been picked up by Gizmodo.

Web visitors weren’t just looking at the piece; they wanted copies for themselves. Along with the flood of page views, there was also a big bump in poster sales for All Streets and the related Dencity poster, with all proceeds going to charity. Of course, this created a (very good) problem of handling all the shipments. Terrence stepped up to lead a team of conscripted volunteers to roll, pack and mail the 130-odd posters over the course of a couple afternoons.

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Mark and Varounny are ready to roll!
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Only 107 posters to go…
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Our mailman was thrilled.

Things have returned back to normal after the rush, and we’re dreading (hoping?) for another one. In the meantime, new hire Brian has taken to calling himself Fathom’s Ambassador to Reddit, and is cranking out Ben Fry memes at a somewhat disturbing pace.

No Ceilings: The Full Participation Project is committed to using data as comprehensive evidence to measure the status of gender equality around the world. Our latest video for the Clinton Global Initiative uses data to demonstrate the progress of women and girls since the UN World Conference on Women in 1995. While the video gives a high level summary of CGI-related topic areas, we found it important to share a more granular, interactive version of the findings that fed into the piece.

Education

As the initiative is gathering data on the participation, completion, and performance of boys and girls in school, we looked at the indicator that preceded the rest of the success measures: access. In 1995, girls had less access to primary school than boys, and the disparity was most drastic in areas like Sub-Saharan Africa and South Asia. Over the last two decades though, net primary enrollment for girls has grown by 25% in both regions.

Download data (.csv)

The video features net enrollment ratios rather than gross enrollment ratios so that we can understand the proportion of students who are not in school who otherwise should be. Net ratios measure the number of students who are enrolled in school within pertinent age groups, while the latter gross values measure the total children enrolled regardless of age—meaning repeaters and students entering school at an early age can distort the actual disparities that exist between genders.

The map above displays secondary enrollment rates for girls, revealing darker (lower) rates for girls in places like Sub-Saharan Africa and South Asia. Built into a rotating globe in the presentation, countries populated with relevant time-series data throughout the course of the video. The globe acted as a constant seam that stitched together additional layers of country-level information.

Wages

Comparing income between genders is complicated because it often overlooks the larger structural factors that influence the imbalance. For instance, a disproportionately high number of women are employed in part-time labor as opposed to men, which affects the average monthly and annual salaries between genders. Women are also often employed in different occupations and sectors then men—so a gap in income may reflect the disparity of pay between jobs or economic sectors rather than a pay gap for men and women with the same position. All this being said, the contextual nuances probe a larger question: why don’t women have the same occupations as men?

Download data (.csv)

Even in OECD countries (generally considered to be developed nations), we see a disparity in average annual salaries between men and women. In Ireland, the OECD country with the most “equal” median annual wages, women still earn 3.5% less than men.

 

Unpaid labor

Another imbalance in the workforce is reflected through the amount of time spent in unpaid labor, that being defined as the number of minutes spent on routine domestic work, care for household members, care for non household members, volunteering, and other unpaid tasks. There is data available for a smattering of the OECD countries along with a few others, and in every case, women spent significantly more time on unpaid labor than men. The trend influences the larger gender disparities that exist in the workplace.

There are very few countries worldwide that actually report this data. In fact we have record of only 29 countries in full. Data represents the efforts of governments to report on the status of its citizens. The failure to collect and share information suggests either a shortage of resources to do so, a government’s lack of value for its citizens, or a hesitation to publish the reality of the results. The absence of data and transparency regarding gender disparities in the workforce points to the greater issue—why are there so few countries collecting and publishing gender disaggregated information on the labor force?

Download data (.csv)

 

Workforce

For an accurate understanding of labor force participation rates (LFPR), it’s important to learn the context behind the numbers. The indicator below measures the share of men and women aged 15+ that are a part of the work force. Countries with the highest LFPR for women—like Afghanistan, Albania, and Algeria, where female participation rates exceed 85%— often reflect the lack of freedom and agency of women to choose an alternate path. Extraordinarily high LFPR rates suggest that women don’t have the liberty to complete secondary education, or to select a career with room for advancement.

At a global level and in the following countries, however, the longstanding gap between men and women’s participation in the workforce reflects the greater gender imbalance in economic participation.

Download data (.csv)

A recent study takes the gap in LFPR one step further, and measures the potential economic gains various countries would experience if they equalized employment between genders. While the available data only supports a story on national gains to GDP, the study also correlates women’s participation in the workforce to improvements in literacy rates, access to education, and infant mortality rates.

Download data (.csv)

At 34%, Egypt would experience the greatest percent growth in its GDP by equalizing LFPR. Ranking second, India would increase its GDP by 27%, yet because its economy is so much larger, it would undergo the greatest monetary increase of the countries involved in the study. We measured GDP in purchasing power parity as a metric to compare and normalize economic gains across countries over a single year, 2012.

Download data (.csv)

We filtered a tremendous amount of data down to a handful of high-level talking points, yet it’s important to understand the context, nuances, gaps, and limitations that inform the global stories. Keep an eye out for additional insights on the data. There will be more trends, subtleties, and correlations coming your way.

See video Gains and Gaps: No Ceilings Data Visualization

Today we are announcing the Mirador Data Competition, the goal of which is to make discoveries in large and complex public datasets. The good news is we have been developing a program to help you make these discoveries, it’s called Mirador.

The competition is from September 28th to October 28th, mark your calendars. During this time you can continue to upload findings to your user account from the app. Visit the competition page for complete instructions on how to get started.

The Sabeti Lab is offering cash prizes for the top three findings, which will be chosen by a jury of experts in the respective domains of each dataset.

The official Mirador Data Competition video, check it out below:

We have chosen four public datasets in the areas of health, sports, and global development:

Each one of these datasets is very rich in complex relationships between literally thousands of variables, and even though some of them have been extensively studied by specialists, there is more to be discovered. We also want to highlight the importance of open data as an enabler for transparency and public participation in research, governance, journalism, and economics, just to name a few areas. Please visit the competition website, create an account, and start exploring correlations to win cash prizes!

Last, but not least, we would like to thank the work of our summer interns at the Broad Institute, Mahan Nekoui, who implemented the user submission system, and Tom Silver, who created the intro video.

We’re thrilled to announce our latest project for the Clinton Foundation’s No Ceilings: The Full Participation Project. The video, released this morning at the Clinton Global Initiative (CGI), outlines advancements and setbacks of women and girls over the last twenty years, with particular focus on their access to education and economic participation.

The No Ceilings project is a collaborative effort led by the Clinton Foundation and the Gates Foundation, and it’s committed to using data to evaluate the advancements and challenges facing women and girls since the 1995 UN World Conference.

Our initial data exploration for this year’s CGI centered on educational progress and economic disparities for women and girls. Investing in equal education across genders has positive implications for the health of individuals, communities, and nations as a whole. Further, giving women equal access and participation to educational resources generates greater benefits for national economies. As stated at the conference this morning, “the value of sending your daughter to school is not rocket science.”

Global gap between boys and girls in primary and secondary school education

We’re excited about the project’s commitment to using data to give a comprehensive view of gender equality in the world today. In the words of Melinda Gates, “behind all of these data points are real lives.”

Stay tuned for more on our continued work with No Ceilings: The Full Participation Project.

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September 18, 2014

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What began as a quick presscheck turned into spending the day with Elias Roustom, the Master Printer of EM Letterpress, who is making our new business cards.

Stepping into the shop, the smell of ink brought me right back to my days in the printshop at art school. EM has four Original Heidelbergs, as well as a Vandercook for large format jobs like posters.

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Elias pulls a print from a Heidelberg to see how the paper captured the ink.

Service gaps in the commuter rail schedule “prevented me” from leaving the shop. I learned how some of the mechanics function in the Heidelberg presses, which are really amazing machines. The first Heidelberg debuted in 1913 and many are still running today.

It was also neat to see an experienced printer work through the whole process of a run. It was a stark contrast to when I was in school, and we spent the day messing around with different printing techniques.

If you are asking yourself, “why a whole blog post for just a business card?” (or even if you aren’t) let’s see what Christian Bale has to say.

For more letterpress photos check out EM’s flickr account.

September 11, 2014

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I had some early experience with the problems of a well-tempered scale. Within a few months of learning guitar, it seemed some chords just didn’t sound good unless the guitar was tuned especially for them. This got me interested in the way harmonies and scales are constructed from pitches and frequencies.


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Pitch fever allows you to visually compare the frequencies used in different tunings, and then hear the difference. On bottom is an octave divided into even sixths with a just intonation scale above it. The dots and bars show which notes are currently playing, and their combined waveform is shown at the top.

Briefly, harmonies are groups of notes that sound good together, and they are often based on simple fractions. Scales can be built with simple fractions too, by either applying the math to a single root note, or by repeatedly using the same fraction. However, scales built from the repeated fraction (called “just intonation”) end up with combinations where the notes collide and don’t sound as nice.

A well-tempered scale solves this problem by using a single rule for every interval between notes. This compromises the purity of the harmony, but avoids the nastiest collisions of just intonation.

But what does a well-tempered scale really sound like? I’ve heard various recordings using both kinds of scales, but didn’t have a feel for how bad the dissonances could get with just intonation, or how far away an even-tempered interval could be from a true harmony. I was curious about how simple fractions build into scales, and also wanted to know the fractions that are not used.

To answer these questions, we now have pitch fever. It’s a little HTML5 tool that explores the fundamentals of harmony while comparing some basic scales and the tunings behind them.

September 09, 2014

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Continuing our fascination with color naming across cultures, we set our summer intern, Malika Khurana, on a journey to discover new colors. Color naming, no matter your language, is a verbal process. So one of the driving interests was to see how this could be integrated into a mobile app using speech recognition. In this post, Malika retells her adventure.

The World Color Survey (WCS) was an anthropological study conducted in the 1970s that used color to study the effect that culture may have on language. Field workers surveyed 2,696 native speakers, representing 110 unwritten languages, by asking them to name each carefully chosen set of color chips (many of which are difficult to categorize into our basic colors in English).

Terrence and I took the 330-question survey and found the results compelling. We expected that some colors would be closer in comparison to others, but didn’t expect to have different words for the same colors.

Comparison of my and Terrence’s detailed color profile.
Malika and Terrence’s survey results show on the left comparisons of color blocks that were given the same name, and on the right similar colors that had different names.

For the most part, my colors are consistently darker than his, possibly because I didn’t name a single color “black”. The two of us have very different ideas of what teal and violet are. After some investigation, my “teal” is closer to the dictionary definition of teal, but violet is more loosely defined as anything between purple and blue on the color wheel. No one here supports color brainwashing though — teal is whatever you want it to be!

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We both made up our own names for the unappealing range of greenish yellows (“badness” and “gross”), and when it comes to light blues Terrence uses the the sky as a reference while I reference the water. This may be because I was a competitive swimmer for ten years so I’ve always felt some innate connection to water.

Malika’s complete results after taking the survey.
Malika’s complete survey results.
Terrence’s complete results after taking the survey.
Terrence’s complete survey results.
An example of one of the languages from the World Color Survey where the participant collectively used only three color terms to describe the entire color spectrum.
This graphic is a sample of the results from the World Color Survey. The three color blocks highlighted are results from one language in which the people used only three color terms to name the same colors Malika and Terrence were shown.
These are the 330 Munsell color chips that participants were asked to name in the World Color Survey and in our Colorful Language app. Courtesy of the WCS.
These are the 330 Munsell color chips that participants are asked to name in our Colorful Language app. Image courtesy of the World Color Survey.

Building the app

When I arrived at Fathom, Ben and Terrence approached me with an idea to take the WCS one step further. They wanted to create an app that would make it easy for anyone to take the World Color Survey. The main difference from the WCS is that our app focuses on how people name colors differently within the English language. For example, what I call teal is different from Terrence’s definition of teal.

I built the app in Processing for Android, along with some Android and Java libraries, as it seemed like the easiest route given my previous experience with Java. It was convenient to be able to pull from any of the libraries and implement the same thing at least two different ways, but at times it was tricky to figure out which of those ways was best.

We chose to implement the survey using Android’s built-in speech recognition. Speaking your responses makes the survey easier to complete, and it doesn’t limit users to those who know how to spell. It is also closer to how the original WCS was administered, verbally and in person. Besides, it’s fun to make people think you’re crazy when you’re on the train enthusiastically shouting colors into your phone.

“Blue.”

“Aquamarine!”

“MAUVE!!”

I found a handy example for using Android’s speech API, and then I was off! When you speak a color into the speech recognizer, the app suggests up to three possible words it thinks you said. The WCS looked for single word responses, so I coded the speech recognizer to return up to three possible words and then omit compound words or capitalized duplicates (the speech recognizer counts “blue” and “Blue” as different words). The app then displays the top three remaining results so one can be confirmed.

The app flow for naming a color

Aside from the obvious motivation to name 330 colors in the survey as a way to help us better understand human perception and culture, we considered ways to encourage users to complete the entire survey we created a live updating “color profile” that grows as you name more colors and acts as a UI element.

Visualizations of your color profile data update every time you name a new color.

Each block of color in the detailed color profile is a pixel-by-pixel composite of each of the colors that were given the same name. From far away you can see what your average “peach” looks like, while close up you can see each of the different colors you’ve named “peach”.

From far away, your eyes adjust and average the colors, kind of like a pointillist painting.

To create the color profile I had to regenerate and render a mini data visualization every time the user names a new color, essentially on every page. I very quickly learned the importance of efficient for-loops, especially when rendering such heavy images. To further improve efficiency, the app only downloads a user’s color profile from the server when it is first launched. Once the app is running, it stores and updates that information locally as the user continues to name subsequent colors, and only sends updates to the server to keep it in sync.

A few years ago, webcomic xkcd conducted their own survey to see how people name colors differently.

We have more hypothesis we’d like to test so please, if you have an Android, download the app here and read our other posts about the color kit and the color “grue”.