World-changing ideas travelled fluently along the twenty-one inches between William Playfair’s mind and fingertips. Playfair, the eighteenth-century Scottish engineer who invented the bar, pie, and line chart, relied upon an oft-overlooked digital tool: his hand. He plotted each chart with pen and paper. Borne from tedious hours drawing and measuring, his handcrafted charts describe his world with a visual, economical elegance. Today, we churn out effective charts, also known as data visualizations, with a computer. Like magic, a computer can leap through the variables and the observations within a large dataset, mapping these individual records to a coherent visual framework. Here, the computer conducts our meticulous work instantly. In this essay, I’ll describe some of the computer tools that have democratized data visualization, evaluating their strengths but also offering thoughts to consider when using these tools to tell visual stories.
As a design educator who teaches data visualization, I thought my students would celebrate the computer drawing a simple pie chart for them. After all, those arcs would have taken Playfair actual time to calculate and craft. Yet many of my design students have studio art backgrounds. Even as they’re amazed by the speed and convenience the computer affords, they’re dismayed to discover that we don’t plot our graphs by hand. Here, we’ll take a look at the free applications used by my students, which require little to no coding knowledge. To transform the data, these tools ask for only a cursory knowledge of statistics that’s taught in many middle schools. What do we gain and lose with digital tools that have made charts so swift to create at the speed of thought, with an easy to moderate learning curve?
Before discussing the tools, it’s important to acknowledge the garbage in/garbage out adage. In an ideal world, a creator who we know well has put together our dataset. We know the creator’s intent for recording a set observations about our world and breaking them down into variables, quantitative and qualitative. We also understand their data collection methods to be valid and authentic. In this green world, our records haven’t been summarized for us already. With a mindful approach, this type of data lends itself to digital tools that help us discover and present a story. Today’s digital tools liberate us to mix and remix data, rapidly sketching, exploring a dataset from multiple vantage points. Much like a chef, the data visualization author today needs to understand how to collect nourishing ingredients, prepare them, and choose an effective visualization recipe. Visualization researcher Noah Iliinsky calls effective data visualization a form of lasagna, unfolding its ideas over many layers. Ineffective visualization is like visual spaghetti—just google “data visualization,” and you’ll see abundant examples of elaborate graphics that are impossible to create by hand: often, this visual spaghetti is all sound and fury, signifying nothing, to paraphrase William Faulkner, the prescient Nobel Prize winning novelist for our noisy times.
With all of the bright pixels around us today, it’s easy to forget that pen and paper are inventions too. These inventions, or tools for our real digits, evolved over many years to provide the most utility and ease of use. By sketching your data visualizations by hand, you’re able to see the big picture in a way a computer cannot envision. I promise that you’ll never draw the visual spaghetti you see in Google Images. You also might find some serendipity while drawing. Research shows that drawing is not a talent but a form of intelligence. Unexpected connections appear while thinking visually, along that Silk Road between the fingertips and the mind. In my class, at the beginning of each project, I ask my students to ask questions about our world, which we’ll attempt to picture through data visualization. Students write down their topic, a question for themselves, in one sentence, their goal and audience, and the variables that they have within that topic.
Every project begins with a question. For instance, a student might ask themselves, “How many monarch butterflies migrate in North America each year?” They write that sentence down on paper. Then they draw an actual portrait of their intended audience—a face with distinguishing demographic attributes. Overall, a generalist audience will want simple graphics; scientists will want more elaboration. The variables might include: monarch butterflies, other migrating creatures, monarch lifespans, distance travelled, time travelled, seasons of the year, temperature, geography, types of habitats, flowering plants, predators and prey, human populations, and so on. With this written guide in hand, students can then make predictions about the types of visual frameworks that might match their storytelling. Do they want to show relationships, comparisons, or changes over time? To help explore possibilities, I give them an online resource: the data visualization catalog. To chunk out the story, I often ask the students to write a headline for the audience, taking a cue from journalists. The main idea of the headline can be the main graphic; the secondary ideas in the headline can be secondary graphics.
When sketching the data visualizations with a pen, it’s important to set limits so that you can’t get too detailed. I recommend using index cards to assure a big picture approach, and the biggest, fattest sharpie pen you can find. Create variations on your theme on index cards, surround yourself with them, and see if you and trusted peers can agree on a big picture that makes sense for the story. In every data visualization project, there are two ways of working, as outlined by David McCandless of Information is Beautiful. You can use pen-and-paper and sketch from the big picture to the details, which the computer specializes in, or sometimes, you might want to work on the computer, examine the details, and then produce a big-picture, pen-and-paper sketch, so you can discover what you want to prioritize and edit out in your complex story.
Data visualization can be helpful in helping us answer Who/What, How much/many, Where, and When questions. I call these puzzle piece questions. They are also like layers in your lasagna. Once you put them together, your audience can see a clear picture, and it’s rich and fulfilling for them to consume all of that information. But data visualization is less helpful in answering How and Why questions, the mystery questions that really ignite our imaginations. Often, How and Why questions can’t be visualized because they emanate from invisible belief systems that compel us to interact with each other and our world in certain ways. For nature stories, the How and the Why might be found in the complex, invisible interrelationships of a system, where the whole is greater than the sum of the parts. We can see this in the recent collapse of honeybee ecosystems. Researchers understand that some threads of the complex web that sustains honeybees have been disrupted, but they can’t actually see these threads. An ecosystem can’t be taken apart like a clock. Visualizing all of nature’s parts won’t yield the deeper, more mysterious answers you seek. It’s important to recognize the limits of data visualization during the big picture sketching phases of your project. You can help answer How and Why questions through writing and annotations in your final presentation: text pairs with charts like signposts, guiding the reader through deeper issues below what’s visible. Or you might draw a conceptual illustration, often a visual metaphor, that helps people see the idea, like the iceberg model used in systems thinking.
The world’s oldest spreadsheet: The Kish tablet, ca 3500 BC, Ancient Sumer (present-day Iraq.) This tablet likely records beer allocations. Source: Wikipedia.
Consider the spreadsheet to be a form of writing and storytelling. In fact, writing began not in poetry or novels, but in spreadsheets. In ancient Sumer, scribes used clay tablets to record foodstuffs and other legal matters. Perhaps picking up on our horizon line that divides earth from sky, these scribes divided their information with horizontal lines that resemble rows, and vertical lines that resemble columns. Despite providing the seed for writing and boasting an ancient lineage, spreadsheets languished as design interfaces for many years. Most people associate them with arguably negative or unpleasant contexts: accounting; cells; formulas. That’s why I offer a resounding thank you to the design team at Google Sheets for making its interface and tools so friendly.
While Google Sheets doesn’t offer the powerful functionality of Excel, its rival spreadsheet application, it boasts a winning user interface and experience. My students are able to perform the same tasks in Google Sheets that had befuddled and even angered them in Excel. With the green Explore button in the lower right corner of the spreadsheet, Google Sheets also allows you to automatically explore data through descriptive analysis and charts. You simply click in a cell on the spreadsheet, then click “Explore,” and new vantage points on the data appear in a panel. This panel even tries to predict the type of question you might have about the data. You can add one of these charts to your spreadsheet or you can then highlight your data, chart it, and then use a free tool such as SVG Crowbar to download that chart for further manipulation in an art application such as Adobe Illustrator. Like Excel, this tool also allows you to summarize your data through pivot tables, so that you can attain an appropriate level of detail for the story you want to tell. By taking original records and chunking them together into sums and averages within a single variable, you have a better chance of creating a coherent chart that shows relationships and comparisons between categories, rather than the visual spaghetti often associated with data visualization.
Visual spaghetti ultimately leaves you entangled and unfulfilled. Yet despite its necessity for visualization, summarized data also presents dangers: it molds together individual records until you can no longer see them with the clarity you had before. When summarizing your data using pivot tables, remember that your spreadsheet is a form of writing that has the potential for coherent meaning, and just beware of the danger of a single story, as noted by the acclaimed novelist Chimamanda Ngozi Adichie. Here, Adichie talks about stereotypical views of Nigerians, her home country, and how a novelist needs to see every person in their vivid individuality. Even with our computer tools, we need to also find the humanity in our data. A brilliant example of that can be seen in Hans Rosling’s famous presentation comparing the health and wealth of two hundred countries around the world over the past two hundred years in four minutes. At one moment near the end of the presentation, he shows China’s summarized data plotted on a bubble chart. But then he plucks China’s bubble apart. Some parts of China are similar in their health and wealth to Western Europe, while others are similar to Sub-saharan Africa. If we had only the summarized data—so easy to do in a spreadsheet application—we would have lost the diversity of voices that comprise China in this story. While spreadsheets can take messy data and help you clean them up, pay keen attention to what might be lost in this process, and always preserve your original dataset on another worksheet tab.
A simple alluvial flow diagram visualizing the animal kingdom in Raw: kingdom, phylum, and class. Beware of visual spaghetti when the computer charts for you. Figure out the level of detail, or summary, that you need to tell your story, and what design techniques you can apply to help people chunk the information and see the signal in the noise.
Here, I simply added order and family, more detailed levels, to the visualization, and the unfortunate result is visual spaghetti. How much detail , or chunking, do you need to tell your story?
If you want more knowledge of D3, I recommend Scott Murray’s book Interactive Data Visualization for the Web, which does a surprisingly humorous job of leading the reader through its intricacies. Even though it is thought of as library, D3 is meant to be a custom creation, and this requires writing a lot of code. A simplified version of D3 can also be studied and implemented in your projects: d3plus, which is often used by MIT for its data visualization interactives. While d3plus does require coding, it uses less lines of code to build interactive data visualizations, which can be useful when the bespoke approach of D3 seems too unwieldy.
iNZight Lite is a free, eponymous academic tool developed at the University of Auckland in New Zealand. iNZight was initially developed as a visualization package for R, the world’s largest open-source software for working with data. R is all text-based in its interface, and it was developed for people working in statistics. It functions like a swiss army knife: by installing packages, you can customize R for your own needs. iNZight has matured into an easy-to-use and beautiful tool for exploring and presenting your data in visual way, without any coding at all. This is true even for the R package, but the lite, or online, version has most of the same capabilities. iNZight has many positive presentation qualities. Its signature paired box plot and dot chart graphs, and its histograms built out of counted dots, are all easy to read at a summary and detail level at once. Each circle represents an individual record, and they are complemented by summaries drawn by boxes and lines. At the same time, iNZight also quickly allows you to explore a large dataset visually. It is especially powerful for faceting your data, creating small multiple charts to compare changes in a single space. Alberto Cairo has several excellent tutorials that will introduce you to the tool, from single variable to multivariable plots. Again, all of these charts can be downloaded into more artistic image editing tools, such as Adobe Illustrator.
As mentioned before, R is not just a pirate’s barbaric yawp—it’s also the open-source swiss army knife for working with data. While it has a reputation for a steep learning curve, you can actually make effective visualizations with just a little training. To learn R and my favorite visualization package, ggplot, simply google Hadley Wickham, the inventor of ggplot and a great educator on how to use R and ggplot together, along with other packages that he invented for organizing and exploring your data. R is best to use when you have epic data sets that spreadsheet applications, such as Google Sheets, can’t handle, or you want to plot your graphic in an unusual way, such as on a polar grid. R allows you to be creative and think outside-the-box in how you layer together and plot your visualization. You can even build an interactive using R that works online with web standards—no HTML or CSS knowledge required. The package for that is called Shiny R.
Imagine the Google Sheets interface—vast territory devoted to columns and rows, with the opportunity to insert charts around the spreadsheet—suddenly inverted. That’s Tableau Public, with its emphasis on charts over spreadsheets. Once you connect to your data in Google Sheets, Excel, CSV or TSV form, Tableau automagically interprets your data into numbers, strings for text, and latitudes and longitudes for mapping. On your first worksheet, this data is broken up into two stacks of blue and green pills. The blue pills are discrete data, or categorical data that Tableau calls Dimensions. This creates buckets for you. The green data is continuous, which draws axis lines for mapping the buckets of data into space. Tableau calls this data Measures. To sort through all of these variables, Tableau offers a Show Me panel, where you can select some of these pills and see appropriate visual frameworks for them. With your variables selected and just one click on a suggested chart, you can create a visualization, which can then be customized visually on the Marks landing pad. For instance, you can create a map of tornadoes in the United States, with circles placed according to automagically geolocated data, scaled according to number, and colored according to miles-per-hour in speed, from dark to light blue. Typically, this secondary level of mapping information works for a broader, more general question that you want to see visualized, while your most important question can be seen through one of our most powerful visual attributes: position in space showing relationships, comparisons, and connections.
Tableau is effective for rapidly sketching variations from your data set, and then linking them together into interactive dashboards. If you were a chef, Tableau helps you avoid making spaghetti out of your data. Instead, you can create layered, rich stories, just like visual lasagna. The primary drawback with Tableau is that it’s a closed system, meaning the language encoding your graphics is proprietary to Tableau. While you can connect Tableau to R and it allows you to work with shapefiles used by mapmakers, it is not as connected to web standards that are built using HTML and CSS. Newcomers to data visualization, such as Quadrigram and Plotly, are using web-based methods and work completely online. They show the effects of modern-day interactives, with smooth transitions a most notable improvement over Tableau for beauty, ease of reading, and comparing variables. Hopefully, this competition will compel Tableau to create a web-based version of its software, which integrates more seamlessly with web developers.
Communication theorist Marshall McLuhan offered many timeless nuggets of wisdom. First, he said that the medium is the..
We at Graphicacy sometimes have difficulty describing just what it is that we do. The term “creative analytic guides” is one we’ve used that I think gives a good overview:
creative, because beautiful, effective design is our foundation and core to all of our projects;
analytic, because we’re often tasked with presenting complex information that requires wrangling, synthesis and transformation;
and, guides, because we lead our clients through a process of discovery, design, and technical work that is based on well established practice and yet still always follows the unique contours of every new project
See? That was already longer than an elevator ride, and you might still be asking: “Yeah, but what do you do?”
Truth be told, we conceive and build lots of different things: charts; maps; animations; static graphics; small, lightweight microsites; big, complex websites built on content-management systems; interactive bits that are hard to define that you can click on, activate, filter, and play with. Most projects we work on are actually several of these put together. So, when someone asks us what we do, that’s challenging to answer. Build websites? Well, yeah, kind of. Data visualization? Yes, frequently, but that’s not all. Graphic design? Always at least a little bit, and sometimes that’s the whole project.
Hurricane Sandy page on the Climate Signals site
A great example of this was the Climate Signals website, which we built in partnership with a great climate change and energy communications group called Climate Nexus. The Climate Signals site is a clearinghouse of information about big picture climate phenomena (increased temperatures, changes in rain- and snowfall, etc.,) the specific real-world impacts of those phenomena (individual storms, droughts, etc.) and the science behind the connections we can make between the two.
One of Climate Signals’ cause-and-effect diagrams for climate change impacts
You can see this in action by taking a look at one of the events tracked by Climate Signals – Hurricane Sandy. The first thing you’ll see examining this notorious storm within the Climate Signals site is a page with a thorough set of text and visual content. There’s a description of the event and the best current scientific understanding of its causes, backed by academic citations and references.
Hurricane Sandy has a life on the site beyond this page, however. A short distance down Hurricane Sandy’s page, for example, is a diagram showing a complex set of relationships – a chain that connects the warming of our planet due to increased greenhouse gases through its many knock-on impacts to the specific natural effects that contributed to Hurricane Sandy. In the context of diagrams like these, which appear throughout the site and portray numerous events and their causes, Hurricane Sandy is a node in a visual web of cause and effect.
Hurricane Sandy mapped along with other climate change events
Still, Hurricane Sandy isn’t done yet. It also appears on the homepage of the site, as an item on the map of recent and significant past climate events. In this context, it takes the form of a location alongside many others that reveal climate trends at the level of communities, countries, regions, and the world overall. Here we can use Hurricane Sandy’s presence and proximity to other events to understand the variable ways in which climate change is impacting, for example, the United States – hurricanes, flooding, and blizzards in the Northeast; drought and wildfires in the Southwest; etc.
Hurricane Sandy and Climate Signals are thus a useful way to understand the larger context of what it is we do here at Graphicacy. Information, whether it’s numeric, narrative, geographic, visual or otherwise, when put on the Web can take numerous forms, often simultaneously. The challenge at hand, working in concert with our clients, is to understand that information and then link it expertly with its best possible displays, from a single embedded chart all the way up to a whole website, and often several things in between.
So, that’s what we do – we give life to information in a variety of forms. We strive to ensure that it is engaging, clear, accurate, persuasive, and beautiful, and we use whatever tools and create whatever outputs let us achieve that. That’s why we’ve had the good fortune to get to work on unique, complex projects like Climate Signals … and, why we haven’t quite nailed that elevator pitch.
Graphicacy’s Creative Director, Jeffrey Osborn, and Technical Director, Kevin Lustig, recently had the opportunity to record a podcast with Robert Rouse, Analytics Consultant at InterWorks. The discussion ranges across a variety of data visualization related topics including the recent Tapestry 2017 conference as well as some insights related to Graphicacy’s client roster, use of digital tools and overall approach to creating effective visual storytelling with data.
Podcast Your Data interviews leaders in the data and visualization communities. Visit the InterWorks site to check out other great episodes.
Recent events have us thinking about immigration and a data visualization project Graphicacy designed and developed for the Center for American Progress (CAP.) After commissioning a large study on the impact of immigrants and their children on the U.S. economy, and with critical immigration related legislation pending on the Hill, CAP wanted to create dynamic, data-rich tools and narratives to share this information with policy makers, the media, academics/researchers as well as a more general audience of voting age Americans. With these end users in mind, Graphicacy created an online package including a 3 ½ minute motion graphic video and four distinct interactive information graphics woven together with short text.
A little context
America is in the middle of seismic demographic changes. As Baby Boomers (mostly white) age and begin to leave the workforce in large numbers a few key things can be noted –
American is becoming more and more a nation “of color.”
Over the next couple of decades, native population growth will not generate the numbers needed to fill new and replacement jobs in an expanding workforce. Immigrants and their children will play a major role in filling this gap – “…the economy will increasingly rely on a younger and more diverse generation to strengthen our workforce.”
Asian & Pacific Islanders and Hispanic workers will represent the highest rate of change as they assume an increasingly important role in America’s job landscape.
Windows on the data
The Graphicacy team suggested representing CAP’s immigration data through a series of different “windows” that afforded views on specific key elements. We encountered some specific advantages and challenges related to the various chart types along the way.
Our Future Together: How Immigrants Will Reshape Our Workforce - YouTube
Motion graphic video
Using the metaphor of a giant oak tree to represent the American workforce, birds as immigrants, and acorns as the fruit (jobs, products, companies formed) of these immigrants, we created a visual narrative that was intended to carry the core story of immigration’s importance to the health of the U.S. economy (past, present and future) in a way that could be easily understood and appreciated.
A large tree seemed appropriate for the topic given that waves of assimilated immigrants lie at the very roots of our nation’s history, a massive trunk quickly represents the scale of America’s economy and at the edges and top of this dynamic, ever changing system are the tender buds and leaves that represent future job growth requiring infusions of new talent from home and abroad.
The use of metaphor, along with beautiful flowing animation effects, allowed us to present the key facts in an engaging way that we hoped would soften any predisposition to prejudice in our viewers and open them to hearing the underlying message vital to their own future financial wellbeing.
Interactive pyramid chart
Pyramid charts are good at showing the changing “shape” of a demographic group over time. Aging/maturing groups are bulging at the top and middle of the graphic (like Baby Boomers) while more youthful groups spread out wide like a Christmas tree at the bottom of the graphic (like 2nd generation Hispanics and Asian & Pacific Islanders.) In a quick glance users can tell who’s on the way in as far as numbers and influence, and who’s on the way out.
The interactive options we provided allowed users to “find themselves” in the graphic in regards to generation, race and time. We hoped that this might provide an opportunity for people to experience a more heartfelt, personal link with the information.
Due to the vastly different number of people in each of our demographic breakdowns, we did find a challenge around keeping each of the pyramid charts easily readable in terms of their shape (particularly for smaller groups like Asian & Pacific Islanders) while fairly representing the difference in scale. With a frequently changing x-axis, we ultimately felt the need to prominently place text below each graphic spelling out the actual numbers and percent of total workforce to help in this regard.
Interactive tree map
Tree maps can be useful for comparison between broadly defined groups (like race.) Our immigration graphic of this type also afforded interesting glimpses into the relationship of sub groups (like gender within a racial group) over time.
Although helpful in quickly showing larger changes, the tree maps proved to be a little more challenging to follow when changes were subtler and when users were quickly toggling through multiple category combination options.
Interactive line chart
Line charts lend themselves to showing change over time. We used this information graphic to show the richness of the entire data set (a line drawn for every category combination) along with numerous filtering options to create specific views of change over time as a percentage as well as actual numbers within the overall workforce.
Using the filters to select a primary line for viewing, users also had the option to mouseover other lines in the graphic to highlight for comparison. One of the challenges with this chart choice was the density of lines clustering and overlapping at the lower end of our change-in-percentage y-axis, which made direct line choice difficult where we had this collision of data.
Interactive stream graph
This graphic allowed us to provide historical context for immigration in America in a dynamic way. The ebb and flow of immigrants from different geographical regions and countries was visualized over the span of almost two hundred years of data against the backdrop of historical (like World War II) and legislative (like the Immigration Act of 1917 codifying the full exclusion of people from Asia) events that shaped the contours of the groups being charted.
Graphicacy works with many mission-driven organizations like non-profits and think tanks. This means that we’re often juggling the commitment to integrity as data visualizers (representing data fairly and accurately) along with the understandable desire of our clients to present their point of view in a compelling way. We had a great experience in this regard with the CAP team.
The data set was researched and reported by a highly respected team of academics so we knew our raw material was coming from a credible source. CAP wanted to share a story from this data that underscored the importance of current and future immigrants (and their children) to the health of America’s workforce.
Immigration, even though it defines a huge part of our nation’s history and many of its success stories, remains a sometimes highly charged and volatile topic. The “other” can create strong feelings of anxiety and fear for us humans. Data (credible evidence) allowed us to ground this data-driven narrative with actual numbers, source countries, context of historical events, significant contributions, etc. and give credence to the need to support the safety, health and education of immigrants in service of a better future for us all.
Here are three behind-the-scenes reflections from the update process:
1. Do it in code
Even for a static poster, the benefits of creating the poster with code are large. One of the primary advantages of this is to make updates more efficient. Another is being able to standardize elements across the entire poster (for example, ensuring all elements use a common time axis and color coding). This is particularly important for large products such as posters.
This poster is almost entirely created in R using the plotrix library. While R is generally used for working with data, it also has a wide range of design capabilities. In this case, final embellishments and layout adjustments happen in Adobe Illustrator, but the majority of the finished product is produced by R scripts.
2. Effective visualizations of history give a sense of perspective
The best works of history help readers/viewers/users take the long view, zooming out from the current moment and putting things in context. We aim to do this with our Timeplots posters. For me personally, working on this poster provided perspective on Donald Trump’s presidency, seeing it in the larger context of a long line of American presidents (which might be comforting or alarming, depending on your perspective).
3. Visualizing history and politics requires many subjective choices
Creating a historical political poster is in some ways a journalism project, requiring the creator to be a thoughtful editor of the vast array of information that could be included. This includes big questions such as what topics to focus on and how much prominence to give each (this poster covers a range of subjects, including elections, the economy and spending, wars, the Congress, and presidents’ experience).
This also means carefully choosing specific data points and how to display them (for example, which economic measures to highlight). One update we made on this poster was adding presidents’ business experience, which helps gives a fuller sense of presidents’ prior experience and helps fill out Donald Trump’s otherwise thin biography. Each choice like this involves knowledge of the topic at hand, and some amount of subjective judgment.
Head over to the Timeplots site for more details and to get your copy.
Hi, I’m Yinan. Summer of 2016 is over and my first on-site internship has come to an end. As a design intern at Graphicacy, I participated in many design-related tasks, varying from photo cropping to UI design for web-based interactive modules. I also contributed to the design of a promotional brochure for a media company and made a motion graphic video illustrating the process of client projects at Graphicacy:
Visual storytelling with data, our process - Graphicacy - Vimeo
I felt lucky to work for Graphicacy as my very first internship. It’s a small team with flexibility to customize the internship experience. There are projects in graphic design, motion graphics, interactive design, UI/UX and programming. My assignments were well-aligned to my interests and goals. Also, I joined in several projects at different phases and have experienced almost the whole process of a client project from concept development to launch. I’ve had the opportunity to sit in meetings with clients and potential partners, which helped in getting to know the real business world. Besides, whenever there was something worth learning (about client relationships and working philosophy, for example), the team took time to point it out to me. I was lucky to have such a nice group of colleagues for my first working experience.
My primary take-aways from this internship are getting to know the process of commercial design projects (to be specific, data viz related projects), the business development of a company, client relationships and how different positions within a team collaborate. For example, now I know how an interactive data viz project is executed by a data analyst, designer and developer under the coordination of a creative director. Each person in these roles has responsibility at a certain stage but also collaborates with the rest of the team throughout the process to make sure the end product is logically correct, visually appealing, accurate, feasible to program, and facilitating a great user experience.
Also, I’m getting to know the art of communication with clients and potential partners — such as how to craft estimates at the start of a project and how to find opportunities for collaboration. It’s interesting to work with people in different specializations because they think very differently and notice issues and possibilities I’m not aware of. Overall, I think my experience at Graphicacy was successful and quite valuable to me. It was a great summer!
Earlier this week, Climate Lab Book posted a wonderful (and terrifying) climate change visualization that swept the internet, or at least the parts of the internet that care about climate change and like cool charts, as we here at Graphicacy do.
The graphic visualizes global temperature change since 1850, with a mesmerizing spiral expanding outwards showing the earth’s warming:
There are a number of great things to note about this graphic, including the clean design, effective use of color, and animation. But what’s most remarkable is that everyone has already seen this data.
Here is the exact same dataset presented as a line chart:
(Yes, we made this in Excel)
This should look familiar to anyone who regularly reads about climate change.
So why did the spiral chart go viral? Is it because it communicates something new that people didn’t already know? Or is it because people like mesmerizing gifs?
One advantage of the spiral is that it highlights the drastic temperature increase of the past year. By overlaying years, those with extreme temperatures (2015-16) stand out, while the others blend together.
But this is all information that is contained in the line chart. In fact, the line chart does a better job of communicating the nuances of the historical trend. The dominant message of the spiral is that the earth is warming, and 2015 was the hottest year on record – something that most people who saw it probably already knew.
So why did it get 10,000 retweets? For those of us in the data visualization business, this is an important question. Here’s a possible explanation:
– The spiral is beautiful. The spiral is an interesting shape in striking colors. The line chart is fairly unattractive – we could add color but it probably wouldn’t help much. On the spiral chart, color corresponds to time rather than temperature as might be assumed – one could argue with that decision but the effect is pleasing to the eye.
– The spiral is mesmerizing. The spiral is fun to look at, and this matters. It would be fun to watch even if it weren’t telling an interesting story. Animation is a useful tool to convey information and draw an audience in, and gifs have untapped potential as a data viz medium.
– The spiral is an interesting way to visualize the data. I think this, more than anything, is the significant piece. People like graphs. People enjoy looking at colored shapes that represent data and figuring out the connecting logic. Understanding the visualization logic of the spiral is a miniature brain game that makes us say “aha.” The line chart, for all it’s usefulness, is something we have seen many times, and doesn’t provide us with any new puzzle to solve.
In a great piece on “the inevitability of data visualization criticism,” Andy Cotgreave explains that every visualization could be a line chart or bar chart, but a successful visualization must be both clear and engaging.
Even a dataset we have seen before can be engaging if it is made beautiful and mesmerizing and uses a visualization approach that makes the reader say “aha.”
Come one, come all! Graphicacy will be announcing the winners of the Major League Data Challenge: Visualize the Presidential Nomination on Wednesday, May 25th at 5pm at the Data Visualization DC Meetup. Make sure to sign up for the event here.
Our contestants entered from all over the world, so we are very excited to reveal the winners. The entries showcase many interesting points in what is commonly referred to as the most unique presidential race of all time.
As with all of our contests, we hope to help users better understand key topics in areas such as sports, politics, science and current events. We believe our contestants did just that!
We hope you will join us for this night of data, design, and drinks. Be sure to bring your analytical and creative thinking caps to discuss the winning entries.
Hi there, my name is Jenny. I’m the marketing, development and design intern at Graphicacy this semester. As you can see from my notebook, I think visually.
While I’ve been working here, I’ve learned that developing creative and effective solutions to present data is the core DNA of Graphicacy. To stay at the forefront of new technology and advancing creative approaches to data, we look to many places for inspiration on a day-to-day basis. And while this process of discovery is key, it is also crucial to never forget the basics and origin of information design and data analysis.
The overflowing bookshelf is one of my favorite parts of the office. Whenever I need a brain break, I can look at an overwhelming amount of amazing books on visualizing information. The best part about this is while I am getting lost in beautiful information graphics, new ways of explaining old things, or even focusing on the wonderful book design in general, I am getting more and more inspired.
Luckily, Nathaniel has written a list of his top recommended books on data visualization. These books are the most important to Nathaniel and his data visualization journey. Like I have from reading and flipping through these books, you will learn about many different approaches to data and design.
Whether you are just joining the world of data visualization, or an expert in the industry, these books are are sure to spark the beginnings of a great next project. Head over here to download your copy of Great Books to Inspire Your Next Masterpiece of Visual Explanation by Nathaniel Pearlman.