January 27, 2015

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One of the great things about working at an information design firm like Fathom is the opportunity to work with subject matter from all kinds of domains. In a single week, we’ve researched topics ranging from social issues that affect women’s equality, database management, and student debt. Recently we were even pulled into the vortex of an international cartographic mystery…

Zooming into a twenty-mile-wide array of circular roads, only to find... more circular roads?
Zooming into a twenty-mile-wide array of circular roads, only to find… more circular roads?

As our Twitter followers know, we’ve expanded the All Streets poster series to cover select countries around the world. The maps outline the geography of various states and countries by plotting only their streets. Of course the data is gathered and processed programmatically, but the maps still require human design attention after the numbers are crunched. To that end we give each poster a once-over for any odd patterns or marks that look like glitches rather than actual streets. It was this process that led us to the oddities in the southeastern tip of Alberta, Canada:

That's no moon...
That’s no moon…

A series of roads in nearly perfect concentric circles, over 20 miles in diameter at their widest, was sitting in the middle of rural Canada? We thought there must have been some kind of error. But when we double checked the source at OpenStreetMap, there it was: a label reading “CFB [Canadian Forces Base] Suffield,” enclosed within a foreboding red-striped area. A quick glance at Wikipedia told us that Suffield is “the largest Canadian Forces Base and the largest military training base in the Commonwealth.”

CFB Suffield, with its largest and outermost circular roads
CFB Suffield, with its largest and outermost circular roads

We switched to Google Maps to get a satellite view of the area, and quickly located the circles (although they didn’t appear in Google’s street maps). Looking a little closer, we noticed even more circles inside the original ones. How curious! But upon zooming in to get a better view, we were surprised to see even more circles! No less than nine in total. And at the center sat an oddly shaped formation or structure.

Zoomed all the way into the circles, a small structure/formation comes into view
Zoomed all the way into the circles, a small formation comes into view

We searched for more information about CFB Suffield, and found out it was established as a military base under the name Experimental Station Suffield during World War II for chemical warfare training. Currently it hosts a laboratory of Defense Research and Development Canada. We also discovered that we weren’t the only ones asking about the strange circles. Other mapping aficionados, local residents, and conspiracy theorists had noticed them too. Unfortunately, we weren’t able to uncover any definitive answers, although some of the speculation is intriguing.

From a forum discussion about the Suffield circles. Did we just get put on a list somewhere in Ottawa?
From a forum discussion about the Suffield circles. Did we just get put on a list somewhere in Ottawa?

So what’s the deal with the circles? Are they just roads after all? Divots from explosives testing? A giant particle accelerator? Or maybe the conspiracy theorists are right and it really is a landing pad for Canadian UFOs.

We're definitely on a list now
We’re definitely on a list now

Visit our print shop to get your own copy of the All Streets map for Canada, and use the code “ALIENS_AMONG_US” for a $15 discount!

Or check out maps of other countries and U.S. states.

January 06, 2015

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Since its initial launch in the spring of 2014, we’ve recently finished updates to the Poverty Tracker. The tool, built with Robin Hood, shows how the Official Poverty Measure (OPM) underestimates the number of New York City residents suffering from financial poverty, material hardship, and health challenges. The recently developed Supplemental Poverty Measure (SPM) gives a more accurate depiction of what it means to live in poverty by considering location, modern-day spending habits, and varying sources of income. The latest update incorporates new survey results from Columbia University and Robin Hood to show how poverty is related to health and neighborhood services.

The Poverty Tracker measures the distribution of the population that experiences various forms of poverty and hardship, yet it’s interesting to look further into the data to measure the likelihood of different experiences within each demographic.

The latest update delves into the relationship between poverty and health in New York City. While the proportion of residents at or below the poverty line experience chronic illnesses at a similar rate to the proportion living above the SPM line, those living below are more than twice as likely to be uninsured. To make matters worse, those below the SPM line are 20% more likely to be hospitalized.

While issues with health are tied more directly to poverty levels, problems with neighborhood services are cross-cutting. New Yorkers across income levels reported poor local services in health,  sanitation, transportation, crime, and recreation.

Check out the site to learn more about poverty in New York City, and see the latest updates.

December 12, 2014

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Last week, some of us here at Fathom had the privilege of attending the Massachusetts Conference for Women, where we saw Secretary Clinton and Lupita Nyong’o speak.

The Massachusetts Conference for Women is a yearly conference focused on bringing together the network of female community leaders, workers and entrepreneurs. The event is made up of resume building workshops, company booths, and keynote speaker presentations. This year, the keynote speakers included Lupita Nyong’o and Secretary Hillary Rodham Clinton.

Lupita Nyong'o addresses 10,000+ women at the MA Conference for Women
Lupita Nyong’o addressing the 10,000+ women at the MA Conference for Women

Ms. Nyong’o spoke about her journey to becoming an actor. Her talk focused on the importance of acting on your aspirations, no matter how lofty, in order to move forward.

Secretary Clinton thanks Lupita Nyong'o for her inspirational speech.
Secretary Clinton thanks Lupita Nyong’o for her inspirational speech.

Secretary Clinton focused on Massachusetts as a place for progress and change, particularly for women. She brought up historical Massachusetts female pioneers such as Emily Dickinson and Abigail Adams, and praised Massachusetts for recently passing the law granting workers paid sick leave. She encouraged the women of Massachusetts to continue being engines of change and to “continue to crash through ceilings, and unlock the unlimited potential of every woman.”

Hillary Clinton addresses the audience at the MA Conference for Women
Hillary Clinton addresses the 10,000+ women at the MA Conference for Women

After the session, we had the pleasure of meeting Secretary Clinton and briefly discussing the data initiative we have been working on, which charts the global progress and setbacks of women and girls over the past twenty years. Stay tuned for more updates!

December 05, 2014

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We are very excited about the release of our latest poster, Scaled in Miles. Based on one of the greatest jazz musicians of the twentieth century, Scaled in Miles looks at Miles Davis’ career through a timeline of his recording sessions and the musicians who collaborated with him. Take a look, tell us what you think, and order your poster today.

After first building an interactive visualization that lets users explore and listen to Miles Davis’ many collaborations, we decided to design a printed poster to see how a single set of data can be designed and tailored to different mediums. In the print version, the data evolved into the shape of a record, with thin arcs marking the grooves, and the circular shape representing the timeline of Davis’ career.

Measuring 24” x 36”, this offset poster is printed on 80# French Construction Nightshift Blue with two impressions of metallic gold and light blue opaque inks printed on both sides of the paper.

Scaled in Miles, hot off the press at Signature Printing in East Providence, RI

The outermost ring shows the timeline of Miles’ sessions, from his first on April 24, 1945, to his last recording on August 25, 1991. Within the outermost ring, the 577 artists that collaborated with Miles are depicted by over 2,000 bars. Each bar represents a musician collaborating in a recording session over time.

Detailed view of Miles’ timeline on the outer most ring. Collaborating musicians are grouped by the instrument they played most, often and stack vertically towards the center of the poster.

Each bar corresponds with the dated sessions along the outermost timeline. Artists who played with Miles multiple times have their sessions connected with thin gold arcs. We called out a few albums that were particularly representative of the genres Miles played during his career. The recording sessions that contributed to one of the key albums is shown in blue instead of gold. We included names for the musicians who either contributed to one of those albums, or who played often with Miles (i.e. nine sessions or more).

Detailed view of two albums called out on the poster: Workin’ and Kind of Blue

The project might sound familiar. Back in April we released an interactive web app based on Miles’ forty-six years of recording, as documented by the Jazz Discography Project. The data encompasses the full personnel for 405 recording sessions, amounting to 577 musicians, and the albums released from those tracks.

to come
Scaled in Miles interactive visualization

One of the reasons we are excited about this poster is that it gives us a chance to demonstrate how the same dataset can take different forms depending on the medium. In the web app, details about the collaborators are revealed as you interact with them.


With this iteration, we were able to include the full timeline of each musician’s collaboration with Miles. On the back, we list each musician organized by the instrument they played most frequently, and chart the number of sessions they recorded on.

Detail of back side of poster listing every collaborating musicians name grouped by instrument and sorted by amount of sessions played with Miles.

If you (or anyone you know) dig Miles, jazz, beautifully printed posters, or shiny gold things, you can purchase the print here. In the meantime, stay cool my friends…

December 01, 2014

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We recently organized the Mirador Data Competition, where participants were invited to explore public datasets in health, sports, and global development using the Mirador tool, submit their findings, and have a chance to win prizes. With the assistance of experts in the areas covered by the competition, we chose three winning entries, and today we have the pleasure to announce them.

The winners of the Mirador Data Competition are:

We believe these three correlations were worth choosing because they give us a glimpse of complex socio-economic processes, and highlight the potential of tools such as Mirador for generating tentative new hypothesis, as well as pointing to their limitations and possible improvements.

Findings were submitted as eikosogram plots, a representation that Mirador uses to explore many variables at once. I’ve gone into more detail about eikosograms in an earlier post. In order to elaborate on the winning entries I created three custom interactive versions that can be explored below. I tried to re-interpret them with a visualization that is better suited for each particular dataset.

First prize: Maria Fernanda Gándara. Outliers in Research and Development Expenditure

Although it might be somewhat expected, the more resources a country invests in R&D, the more people become researchers. But this submission reveals complex patterns of R&D investment and “resulting” number of researchers that vary widely across time and between countries. The visualization below shows a plot of the ratio of number of researchers per GDP percentage invested in R&D next to the original scatter plot. Are some countries more effective at training researchers for a given percentage of GDP investment?

Interact with the charts below to explore this question.

This visualization includes all countries in the Europe, Central Asia, and North America regions, between 2000 and 2013. However, María Fernanda also considered the percentage of secondary female teachers as a covariate in her analysis. You can explore the effect of this variable by clicking the following links to update the plot above. Show countries where the percentage of female teachers is less than 50%, more than 50%, or without constraint. According to María Fernanda “I explored for possible covariates that could strengthen the relationships, and that is how I chose the additional covariate of percentage of female teachers in secondary education. Therefore, the constrain of “percentage of female secondary teachers > 50%” was statistically based (in an exploratory fashion). I do think it can be interpreted, though. Since researchers are typically men, the more women working in secondary education, the more men “available” to become researchers.”

Second prize: Yuliia Khodakivska. The Boys of Mid-Summer?

The second prize entry is a very interesting correlation pointing to the fact that player salaries in Baseball are influenced by many artificial effects, such as fixed pay scales and team caps. The data shows that players born in July have the highest median salaries in the league.

Somewhat related, an article from a few years ago shows that the month of birth for an American League player peaks on August, however this happens only for U.S. born players, non-U.S. players don’t seem to be born in August on significantly higher proportions.

In order to visualize all these patterns, I added the birth counts for U.S. and non-U.S. born players for each month of the year, alongside the median salary as a function of month of birth. All these numbers were derived from the 2013 release of the Lahman’s database.

Interact with the chart below to explore further.

Strangely enough, the peak in the median salary occurs in July, not August as one would expect following the argument in the article. Is this a real effect, or the result of a bug in the code or data? You can download our scripts to check for yourself. Our winner told us about her initial motivation to look at this correlation. Yuliia writes, “an article called How Common Is Your Birthday? (and data source) [...] It contains infographics showing that July, August, and September seem to have more births, comparing to winter times.”

Third prize: Ching-Hsing Wang. Exercise and Health

A correlation between exercise and health could also be considered “expected”, as people who exercise regularly are probably in better health than those who do not (although the link to specific causal factors is less straightforward). The source is the CDC’s Behavioral Risk Factor Surveillance System, which is a phone-based health survey conducted every year to collect data on a variety of factors (demographics, alcohol consumption, employment, etc.)

Here, I thought it would be useful to take Ching-Hsing’s original submission and make it more general in this visualization, allowing readers to explore different association patterns. In order to control, at least to some extent, for confounding effects that might be influencing both general health and exercise, I restricted the visualization to respondents younger than 50 years who don’t report activity limitation due to health problems. It is interesting to see how the proportions of health levels change between males and females and across income groups.

Interact with the chart below and toggle between sex and income.

For instance, the proportion of exercising females who report excellent health is 4% higher than for exercising males in the top earning group. However, this difference reverts for lower income respondents: exercising males report higher excellent health than females. Are these patterns simply the result of random fluctuations in the sample data or due to real effects?

Representing correlations using eikosograms

In Mirador, correlations are primarily visualized with eikosograms. I’ve gone into more detail about them in an earlier post and online documentation. The figure below summarizes how to interpret an eikosogram plot:

The eikosogram on the left represents the correlation between two categorical variables. The height of the vertical columns indicates conditional probability. The eikosogram on the right depicts two numerical variables, in which case the vertical elements are boxplots for each bin in X
The eikosogram on the left represents the correlation between two categorical variables. The height of the vertical columns indicates conditional probability. The eikosogram on the right depicts two numerical variables, in which case the vertical elements are boxplots for each bin in X

The eikosogram is constructed differently depending on whether the Y variable is nominal or numerical. In the former case, the vertical columns represent the conditional probability of each value of the Y variable given the X category. In the former, a boxplot is constructed for each value of X, and the entire eikosogram is formed by all these boxplots placed next to each other. The dark blue box contains values one standard deviation around the mean, while the light blue extends up to two standard deviations.

Although the interpretation of the eikosogram is different depending on the variable type, in all cases it is easy to visually identify a correlation: unrelated variables have a flat eikosogram, because either the conditional probabilities or the boxplots are independent of X. It is also important to note that the scale of the X variable is not linear: the width of each X bin is proportional to the number of samples falling within that bin.

Users in Mirador can also define arbitrary data ranges in order to control by various covariates or to stratify the sample
into subpopulations of interest. Some of the submissions used covariates, while others were reported on the entire sample. The next gallery shows the three winning correlations as they were displayed in Mirador:

Some concluding thoughts

More rigorous analysis would need to be conducted in order to interpret these correlations, but the goal of exploratory tools such as Mirador is to reveal plausible patterns of association, and let users quickly visualize hypothesis based on their intuition and prior knowledge. Any correlation discovered with these tools should be regarded only as a suggestion for further analysis, which is also contingent on the context where one is carrying out the use of these tools: whether in education, research, or applied practice.

It could be argued that one can play with covariates in Mirador until finding a statistically significant association, but as contestant María Fernanda pointed out, this is valid practice in “data-driven” exploratory analysis: the interpretation stage comes later, at which point one can discard the correlation altogether, or conduct further analysis using more powerful tools or better datasets. The feedback received from users so far has been very positive, highlighting both Mirador’s advantages (free availability, ease of use, interactive correlation analysis) and the areas where it could be improved (inclusions of other datasets, better scatter plot functionality, more advanced statistical analysis).

Data and code availability

The code that generates the data files used in this blog post and the JavaScript visualizations is hosted on this repository. Follow the next links to download the individual data files for the first, second, and third entries.


We would like to recognize all the participants of the Mirador Data Competition for their submissions, and Gregory Piatetsky-Shapiro for helping us announce the competition. I would also like to thank the feedback from Tariq Khokhar on the World Bank submissions, Sean Lahman on the baseball correlations, and Pearly Dhingra for the insightful discussions about associations in health data and confounding effects. Finally, many thanks to Lauren McCarthy and the rest of the p5.js team. All the interactive visualizations were created with Processing and ported to p5js.

November 07, 2014

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Because of the immense popularity of All Streets, we expanded our product line, and created maps for individual states. To accommodate our new selection of products, Terrence worked feverishly to design the Fathom Print Shop. The site officially launched yesterday—just in time for the holiday season.

The posters are available in two sizes, 16×20 inches and 24×36 inches. You can purchase the poster with (or without) a frame, and also select from a choice of warm, light, or dark background colors.

Showing solely streets unveils some interesting characteristics about population settlement, topography, and waterways.

Eastern California’s national parks are visible and starkly contrasted from the bounty of roads to its west.
Our home state of Massachusetts has obvious correlations to population density due to the heavy lines around the Boston metro area.
North Dakota’s network of roads are reminiscent of Manhattan’s street grid.
The country’s largest and most populated city, New York, has the densest road coverage near the metropolitan center, leaving a tiny rectangular speck open for Central Park.
Alaska is the only exception to the collection of states we are offering. There are so few roads, they provide insufficient definition for the state. We didn’t originally include Alaska, but after Alaskans complained, we capitulated. Oddly enough, sales of the Alaskan map remain at zero.

For those interested in viewing All Streets for the U.S. territories, we added Puerto Rico and Guam into the mix.

Now that the Fathom Print Shop is live, we’re ready to take your orders!

October 27, 2014

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Public data is increasingly available from multiple sources: governments, economists, and research communities, to name a few. Open access is a fundamental prerequisite for civic participation and transparency, but freely-available and intuitive tools that allow users to extract meaningful narratives from the data are also crucial. That was our central motivation to develop the visualization tool Mirador, and also for the Mirador Data Competition we launched last month. The richness of public datasets is often extraordinary, and many of them are the result of the continued efforts of data collection teams, statisticians, and researchers over several years, sometimes decades. In this post, I would like to share some associations I found using Mirador on a large dataset of behavioral risk factors. These associations stand here simply as suggestive hints or directions that one can use to delve further into the data using more rigorous statistical analyses. This highlights the main purpose of Mirador as a visual exploratory tool.

Many others have recognized the importance of open data and public participation, and had organized similar data challenges or competitions in the past to spur the interest of various audiences in data visualization and analysis. Around the time we launched our own competition, I came across the HHS VizRisk, an event organized by the U.S. Department of Health & Human Services that seeks for visualizations of behavioral data to inform personal and policy decisions. The main dataset in VizRisk comes from the Behavioral Risk Factor Surveillance System (BRFSS), a nation-wide phone survey that collects information about health risk behaviors.

I compiled the BRFSS data made available for VizRisk into Mirador’s format (which is basically a CSV table plus some additional metadata) and did some quick explorations of my own. The screen capture below shows Mirador after loading the 2011 BRFSS data, comprised of around 500,000 respondents:


The happiness of the self-employed

A variable in the BRFSS dataset that I believe is reasonable to choose as a global indicator of well being is “General Health”. Respondents are asked to characterize their health status using 5 options: poor, fair, good, very good, and excellent. So, it would be interesting to look at association patterns between General Health and other socio-behavioral indicators. One association that stood out for me is between General Health and Employment status. This other variable records if the respondent is employed for wages, self-employed, student, unemployed, etc. I call this association the “happiness of the self-employed”, because for the entire sample of 500,000 respondents you can see that self-employment relates with a slight increase in reported excellent general health:


We can conclude that self-employed people are more likely to respond that they have excellent General Health, although the difference is only of a few percentage points. Before going any further, lets first make clear what this plot (called eikosogram) means: the percentage 25.43% is highlighted for the “self-employed” category in the column (corresponding to the Employment Status variable), and the “excellent” category in the row (corresponding to the General Health variable). This means that 25.43% of the self-employed respondents answer that they have excellent general health. In other words, it is a conditional probability that can be denoted in mathematical notation as:

P(excellent health|self-employed) = 0.2543

Mirador is designed for interactive visualization, so only the labels of the hovered items are shown in the interface. For clarity, I have saved the health-employment eikosogram and added the labels for some the categories -employed (for wages), self-employed, homemaker and student:


Next, we can explore what factors might effect this association. Variables such as sex, age, income and ethnic group would probably have an impact on it. It is easy to check with Mirador the effect of any of these factors. For example, the percentage of women reporting excellent health when they are self-employed in relation to employed for wages is higher than for men: 27% versus 24%, while both report similar levels of excellent health for the employed status:


Of course: correlation does not imply causation, but it is worth noting nonetheless. Since we can easily adjust by other socio-economical factors, I searched for a combination of factors that maximize the “happiness” among self-employed respondents.

Age and income have a large effect, with middle age individuals in higher income brackets reporting excellent health among those self-employed. After fixing the covariates in those ranges (age: 35-54, income 35k+), I started looking at the association among different ethnicities, as classified in the BRFSS data: white, black, asian, hispanic, pacific islander, native american, and other/multiracial. What I found is that the highest levels of excellent health for self-employed respondents occurs for the asian ethnicity. The difference between employed for wages and self-employed is quite substantial for this group, approximately 25% versus 44%:


What can we conclude from this pattern in the data? Again, correlation is not causation, but we can wonder if this pattern is due to cultural or economic factors. It is not possible to say from the data, but at least we have a tentative hypothesis we can test further. We also have to be careful with the fact that when control by several factors (age, income, ethnicity), then the sample size decreases dramatically, which makes our conclusions weaker. For example, the number of respondents in the Asian, 35-54 years of age, income higher than 35k, subgroup is of only 2,086. For a visual illustration of the so called “curse of dimensionality,” check this interactive web app.

Better growing old alone… if we have enough money?

Another factor that clearly affects “happiness” is the relationship status of individuals. BRFSS includes a Marital Status variable with several categories, but in order to keep the plots simple I restricted the visualization to Married, Divorced, Widowed, and Never Married. The eikosogram between General Health and Marital Status looks as follows:


The health levels are suspiciously high among the never married category. However, this plot was generated using the entire population sample, which covers all ages starting at 18. By differentiating between age groups and also gender we get a better representation of the change in health patterns among subpopulations with different marital status:


Some of the patterns are expected or known, for example health levels decrease as people age, and the fraction of married women up to 34 years of age is higher than that for men. In addition to that, the fraction of men in the 25-34 age bracket reporting excellent health among non-married individuals is higher (in fact, similar to those married) than for women. Is this a manifestation of the social pressure acting on women to get married before their mid-thirties? Again, we cannot draw these causal conclusions from the data, but at least we can use the visual patterns as a guide for more detailed analyses.

It is also not surprising to find that income levels having a strong correlation with health. But perhaps more interesting is to see how the association between health and marital status dramatically changes its direction when discriminating between high and low earners. When we aggregated all the data for people 55 years or older, we saw in the previous animation a marked decrease in health among individuals that ended up single, either due to divorce, death of partner, or simply by not getting married. But if we now restrict the analysis to people with income levels above $50,000, then there is no longer a decrease, specially among divorced individuals:



I think that these “non-rigorous” findings are a good illustration of the usefulness of exploratory data analysis as a first step. By quickly defining cross-sections of the data and controlling by multiple factors (always within the limits of what the sample size allows) we can use interactive visualization to guide our intuition and find new tentative hypothesis.

If you are interested in exploring the BRFSS and other similar datasets with Mirador, remember that the Mirador Data Competition is still open until next week, and you can win some prizes by submitting your findings!

Finally, over the past months I compiled a list of several publicly available datasets that I included in this public list. Feel free to add more links to the list.

October 21, 2014

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In the last month, we built a tool that explores the global seismic activity occurring over a single year. The project integrates earthquake, population, and mortality risk data so that users can explore how the frequency and magnitude of earthquakes generates varying levels of risk around the world. Visit the site: fathom.info/quakes

Explore a year of earthquakes with the web tool.
Explore a year of earthquakes with the web tool.

I had always thought of earthquakes as dramatic, irregular events, when in fact there are tens of thousands of earthquakes each year that people cannot even feel. Back when I was in school, I made a print piece that looked at the number and magnitude of earthquakes occurring throughout a full year. As you can see in our more recent project, there were over 6,000 earthquakes globally in 2013 (and that’s only looking at those with a magnitude of 4.5+!). In fact, there are over a million earthquakes each year, but most are too small to notice.

A year of earthquakes
A year of earthquakes

Within the dataset pulled from the U.S. Geological Survey, we focused on the earthquakes’ magnitudes, locations, and dates of occurrence. The tool allows you to focus on a particular subset of earthquakes by limiting the timespan, range of magnitudes, and map extent. On the other hand, you can maximize the timeframe and magnitude range to get a picture of the whole year.

When comparing time intervals of the same length (for example 30 days), the ratio of magnitude 4.0-4.9 earthquakes to magnitude 5.5-5.9 to magnitude 6.0-6.5 earthquakes, etc. stays about the same. However, the total number of earthquakes can vary dramatically. A 31-day span starting in January had 449 earthquakes, while the 31-day span starting in February had 809.

The differences and similarities between the seismic activity of two 31 day spans.
The differences and similarities between the seismic activity of two 31-day spans.

The tool revealed hot spots of seismic activity. By including population density as a layer, we could begin to see areas of high risk. For example, Japan and much of East Asia are densely populated along the coasts — an area that is also host to a large percentage of the world’s earthquakes.

There is a great amount of seismic activity in East Asia.
There is a great amount of seismic activity in East Asia.

East Asia experiences some of the year’s largest earthquakes measuring over 7.0 on the Richter scale, putting many of these areas in the top mortality risk deciles.

Two 7.0+ earthquakes in 2013 happened near Japan.
Two 7.0+ earthquakes in 2013 happened near Japan.
The red sections show areas with the highest earthquake mortality risk.
The red sections show areas with the highest earthquake mortality risk.

The largest earthquake in 2013 actually occurred deep in the ocean off the coast of Russia. Luckily, its depth prevented massive damage, but the tremors could be felt thousands of miles away.

An earthquake in the Sea of Okhotsk measured 8.3 and was the largest earthquake in 2013.
An earthquake in the Sea of Okhotsk measured 8.3 and was the largest earthquake in 2013.
Its tremors could be felt thousands of kilometers away in cities as far as Atyrau, Kazakhstan and Moscow, Russia.
Its tremors could be felt thousands of miles away in cities as far as Atyrau, Kazakhstan and Moscow, Russia.

Areas like East Asia, the Himalayas and the U.S. West Coast experience a large amount of earthquakes because these areas lie directly on top of tectonic plate fault lines. On the other hand, areas like the U.S. East Coast, Australia and most of Africa experience little to no seismic activity throughout the year due to their locations in the centers of tectonic plates.

The white lines show the boundaries of the earth's tectonic plates. Most earthquakes occur along these fault lines.
The white lines show the boundaries of the earth’s tectonic plates. Most earthquakes occur along these fault lines.

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.

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.

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.


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.