Data visualization has been around for a long time—far longer than computers have existed—and it is valuable because it allows us to represent a significant amount of data in a way that is easy for our brain to interpret. The first representation of statistical data (below) is credited to Michael Florent Van Langren, a Flemish astronomer. Created in 1644, this one-dimensional line graph shows twelve estimates of the difference in longitude between Toledo and Rome with the astronomer who provided the estimate.
Since then, a lot of progress has been made in the field of data visualization. We’re not going to go over all of those discoveries; rather, this post will focus on several milestones from the last two decades in the data visualization industry.
With the ground-breaking invention of the computer, data visualization boomed. With the genesis of the internet, it exploded. The following three milestones in data visualization are significant for different reasons and will be discussed in some detail here.
Tag Cloud, Word Cloud
Tag clouds, also know as word clouds, started popping up on blogs and websites around 2002 and can be valuable because they bridge the gap between quantitative and qualitative analysis, making data digestible in a semantic form. Credited to Jim Flanagan as the founder of the idea, a word cloud is a group of words that summarize a large group of text. A selection of text is taken and the most commonly used words are displayed in the visualization, the larger words meaning they appear in the text more frequently. They are commonly found on blogs and websites because they make it easier for the viewer to search the site according to what is most popular or most discussed.
Word clouds, however, been heavily criticized, almost since their conception. They’ve been faulted for not really giving an accurate representation of the story they supposedly portray. Critics, especially journalists, add that they are often applied to situations where textual analysis is not appropriate; it just doesn’t make sense to study a complex topic like politics or the Iraq War by simply looking at the words used to describe the events.
Word clouds are worth mentioning when discussing steps made in the world of data visualization, but it’s important to use them appropriately, when the stories they tell are accurate and representative of the information being sought. Ask people to view a campaign ad and have them describe in one word what they thought about it. The resulting word cloud could be significant. Use a word cloud to analyze the similarities between different texts, facilitating research on many levels. On a business level, businesses can run a competitive analysis of their competitors’ websites and see which words are used most. They can then use that for SEO purposes when writing copy for their pages. Word clouds are a tool that, when used correctly, can show some valuable information. Otherwise, the value that they give to data visualization as a whole can be easily misattributed.
In 2004, Edward Tufte is credited with having created sparklines, and described them as “data-intensive, design-simple, word-sized graphics.” They are small, word-sized graphics that are especially convenient as they can be embedded in line with text, tables, graphics, headlines or spreadsheets. They express some series of data or information—like average temperature or stock market activity—in a compact and straightforward way. They usually don’t use axes or numbers or anything but lines that would otherwise complicate the visual and are normally located where they are discussed.
This sparkline, the Mashable velocity graph, shows how quickly people are sharing a particular article on the social web. This is useful because it can show the popularity and success of different pieces of content shared over different platforms.
Sparklines are commonly used to view trends in stock market prices, as shown in this visualization. Viewers are able to make quick decisions based on the tiny graph. Both sparklines present information in clear, succinct ways.
The value that sparklines give to the world of data visualization is clear when we consider that sparklines have been implemented into Microsoft Excel 2010 software as well as a variety of other software libraries, among them Google Chart API and graphing software Origin.
Taking interactivity in data visualization one step further, Trendalyzer, also called Gapminder, is a moving bubble chart invented by Swede Hans Rosling. The information displayed is shown in five variables: two numeric variables on the x and y axes, bubble size, bubble color and a time variable that can be manipulated with a slider. In the image below, you get four bits of info at a glance. The color lets you know the continent in which the country resides, the size of the circle is population, where the circle lies on the x axis is the size of the country’s GDP, and where the circle resides on the y axis is the amount of children that die before age five (notice that the traditional ascending order has been flipped). View Hans Rosling’s TED talk on poverty in which he uses trendalyzer here.
Those who use Google Visualizations API are able to use components of the trendalyzer software, enabling them to express their data in more interesting ways than ever before.
While it’s true that data visualization has come a long way since its inception nearly 400 years ago, there is still much to be done in the industry, especially considering the improvements and innovations in the last twenty years alone. Data Visualization isn’t just for research experts or journalists anymore; these visualizations make it easier than ever for businesses to create simple and straightforward interpretations of data that are quickly and easily understood. Make waves with your data with DataHero’s visualizations today.
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