Humans are built for visual analysis like shape recognition and pattern matching. The easiest way for our brains to receive and analyze large amounts of data is through visualization. It helps us simplify our data, understand it faster, avoid missed opportunities, and learn quickly where action needs to be taken.

We can use it to make information consumable, convey important concepts and ideas, and guide viewers to a desired analysis.

There are countless of examples of data visualization out there, but we’ve chosen 5 of our favorites to share with you — along with what you can learn from them when building your own.

1) U.S. Wind Map

This is a really cool visualization showing all present wind speeds and directions in the U.S. In the live map, speed is represented by lines moving slowly or quickly, while direction is represented by the direction the lines are moving in.

Source: HINT.FM

What we can learn: The wind map is a phenomenal example of minimalistic data visualization. Take note:

  • The monochromatic color scheme is easy on the eyes.
  • 2 variables (wind speed and direction) make it easy to follow.
  • The visual is intuitive. General trends are immediately clear without the help of numbers, which can be a huge distraction. In fact, no numbers are involved unless you hover your mouse over the map, like this:

Don’t fall victim to information overload. When it comes to visualizing data, less is more.

2) World Map of Food Consumption

The Food Service Warehouse created this map of calorie consumption for the most extreme 20 countries in the world, adding the comparison of income spent on food by country.

What we can learn: Again, maps are fantastic ways to depict data in specific locations. If the data above were displayed in a simple table format, we could miss:

  • That 14 of the 20 lowest consuming countries are located in Africa.
  • Where each country’s calorie consumption falls relative to the global average
  • That, of the lowest consuming countries, nearly all spend more than half of their income on food. On the other hand, the highest consuming countries almost all spend less than 25% of their income on food, with the top 5 all under 15%.
  • That Bosnia is a big outlier in the highest consuming countries, spending almost 35% of income on food.

The combination of a map and bar graphs helps us make these insights immediately. When you create your own visualizations, think about the conclusions you want the viewer to draw, and design accordingly.

3) Gay Rights in the U.S.

Gay rights laws vary greatly by state and region, making it very difficult to keep track of which states allow or prohibit which rights. The Guardian does an awesome job breaking it down by state, region, and type of law:

Source: The Guardian

What we can learn: They took a complicated, sensitive topic and simplified it for the average viewer. Our eyes are immediately drawn to a few specific conclusions:

  • We see right away that the Northeast and Southwest allow the most gay rights, while the Southeast, Northwest, and Midwest allow the least.
  • It is easy to see bands of the same color: most states allow adoption by gay parents (blue), and there are decisive laws either allowing or prohibiting gay marriage in every state (red).

They added another cool feature that really takes advantage of the circular form: you can choose to scale the visualization by population, showing us how much of the U.S. population is affected by which laws:

Source: The Guardian

The ability to interact with the visualization…

  • …is fun and intriguing.
  • …causes us to spend more time on the page.
  • …makes us more likely to share it with our friends.

Overall, The Guardian organized and simplified confusing data and made it easily consumable and visually pleasing. Not an easy feat  – and they nailed it.

4) Citi Bike Data

Soon after New York City began its bike-sharing program, The New Yorker published an interactive map of where New Yorkers rode the shared bikes over the course of one month. The map was live, so as the month progressed, the bubbles on the map grew or shrunk according to the number of available bikes at each station. The visualization can be played as a time lapse: as time progresses, the weather and temperature change accordingly, and the map changes from white during the daytime to dark grey at night.


What we can learn: The Citi Bike map is a bubble chart, which is a type of data visualization that comes in handy when you want to depict 3 dimensions of data. These 3 dimensions are usually the x and y axes (or location on a map) and the size of the bubble (used here to indicate number of bikes at each station; in other cases, to indicate dollar amount, population, and so on).

The Citi Bike bubble chart helps us understand how weather and time of day affected bike use. When the temperature was warm and sunny, the frequency of bike movement increased dramatically; and when it rained, movement slowed. You can also see the swell of bikes in certain areas at different times of day: at night, more bikers migrated to Eastern NYC like East Village; during the daytime hours, bikers tended west.

While a scatter plot only charts the x and y axes, the bubble chart adds that 3rd dimension of bubble size. On a bubble chart from an InsightSquared visualization, we added yet another dimension: color of the bubble. Bubble color indicates opportunity risk – green for low risk, red for high risk.

Bubble size and color add multiple dimensions to the classic x-y axis chart and are pleasing to the eye.

5) Disease correlation

Using 7.2 million patient records from GE’s proprietary database, this visualization shows correlations among different types of diseases. In the example below, when you hover your mouse over the “Tonsillitis” bubble, a text box pops up showing that Tonsillitis in females is most strongly combined with strep throat, fever, and flu. Each disease bubble is color-coded by umbrella category (mental health, infections, blood diseases). The key lets you change the visualization to show either females or males, and it also lets you change the entire form from a network to a circular web.

Source: GE’s MQIC Database

What we can learn: This is another awesome example of a bubble chart, only this time, the specific x and y axes of the bubbles don’t really matter as much. Instead, the bubbles are part of a disease network or web — a brand new way to visualize condition associations using patient data. The goal? To help us understand our health and illnesses better. Kudos to GE for coming up with an innovative, interactive way to do it.

Takeaways for creating your own data visualizations:

  • Design your visualization around the conclusions you want the viewer to draw.
  • Use a color scheme that is easy on the eyes.
  • Make animation intuitive.
  • Use a bubble chart when you have more than 2 dimensions to your data.
  • Make it interactive.
  • Keep it simple.

What are your favorite examples of data visualization? Do you have any cool tricks and tips to share?

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