Data visualization is the portrayal of large amounts of data in the form of charts, maps, graphs, or any type of visual format to help you identify relationships and trends in data. While the word “data visualization” might conjure up thoughts of complex visualizations that only data analysts can understand, it’s actually a lot more creative and simpler than you think. There are many data visualization examples - from business dashboards to food to politics to pop culture trends. Good data visualization requires good storytelling and graphic designing skills in addition to data analysis skills. In this article, we will discuss some of the most notable data visualization examples and how they work.
1. Napoleon March Map
Visualization by: Charles Joseph Minard
The Napoleon March map tells the story of Napoleon’s march to Moscow in 1812 to conquer the city of Italy, which was a disaster. He started the march with around 470,000 soldiers and only returned with 10,000 soldiers. This chart has become one of the most famous data visualization examples of all time. The width of the line shows the total number of soldiers. The color represents the direction of the journey - yellow for marching towards Moscow and black for the return trip. It also contains a simple temperature line graph to illustrate the dropping temperature at the time.
2. 1854 Broad Street Cholera Outbreak Map
Visualization by: John Snow
The 1854 Broad Street Cholera Outbreak Map by John Snow (not Game of Thrones’ Jon Snow) is an early example of dot map visualization. It contains small bar graphs on city blocks which show the number of deaths in a London neighborhood. Through this data visualization example, they were able to find out that households that had the most deaths from cholera were all using the same well for drinking water. The well in question was contaminated by sewage and serviced an area with a high concentration of cholera outbreaks. This finding helped the people trace a relationship between sickness and contaminated wells. The solution was to build better sewage systems and protect the wells from contamination.
3. Causes of Mortality in the Crimean War
Visualization by: Florence Nightingale
During the Crimean War in the 1850s, soldiers were dying at a very rapid rate - but this was not just because of the battles. A nurse and data analyst ahead of her time, Florence Nightingale, devised this beautiful data visualization to show that the majority of soldiers’ deaths were actually caused by poor practices in hospitals. The shaded areas of the chart represent the total number of deaths. The darker shaded areas represent deaths in combat. From the graph, it is easy to tell that there was something more alarming than actual combat causing the soldiers’ deaths.
4. How Eggs Get Their Shape
Visualization by: Science
Scientists have recently figured out why different kinds of birds have different egg shapes with data from almost 50,000 bird eggs collected over the past 100 years. The scientists looked at a number of variables, including diet, adult body mass, nest type, nest location, number of eggs in a nest, and a number of flying habits. The study showed that the length of an egg is related to the size of the bird’s body. Whereas the shape of an egg (the asymmetrical and elliptical shape) relates to the bird’s flying habits.
5. Interactive Government Budget
Visualization by: US Office of Management and Budget (2016)
When it comes to government budgets, every country is guilty of making it extremely difficult for the general public to understand. This treemap was designed at the White House during Obama’s presidency to visually break down the United State’s 2016 budget. It certainly isn’t the most innovative treemap nor is it an interactive one - it is a fairly basic data visualization example at best. However, this treemap broke news due to the fact that a major world power adopted a data visualization technique to communicate to its people about where their tax money would go.
6. How Americans Eat
Visualization by: Flowing Data
The United States Department of Agriculture (USDA) keeps a track of food availability and has data from 1970 to 2019. Nathan Yau, an American statistician and data visualization expert, leveraged this data to see how Americans eat and how much it has changed over the last four decades. When did chicken beat beef to become everyone’s favorite? Can pork beat beef in the meat race? What do people prefer - lime or lemon? These are just a few of the questions the chart seeks to answer.
7. The Next US
Visualization by: Pew Research Center
The Next US is another beautiful and interactive data visualization example that takes a comprehensive look at the United States demographic data. This chart shows the percentage of the US population by age group. Each bar represents a five-year age group. At the start of the study, the chart was in the form of a pyramid. By 2060, it will almost turn into a rectangle. This means that there will be as many Americans under the age of 5 as there are over age 85. This is a result of lower birth rates and higher lifespans. While it’s certainly good news for the long run, this also means that it will create a lot of political and economic stress as the working-age group will be pressed to finance the retirements of the older ones and also take care of the younger ones.
8. Film Dialogue (Broken Down by Gender)
Visualization by: Hanah Anderson, Matt Daniels (The Pudding)
The Pudding While Polygraph aka The Pudding visualizes the gender disparity in pop culture using four main visualizations - a breakdown of Disney movies, an overview of 2000 screenplays, a gradient bar that allows you to search for the top-grossing movies and explore some key filters, and the age biases between male and female roles. From the charts, it is clear that there is a stark imbalance in gender representation for every genre. It is easy to notice that men have the majority of dialogue for most movies. In addition to the striking findings from analyzing thousands of scripts, this data visualization project is also notable for its transparency - that white men still continue to dominate most movie roles.
Visualization by: OFFC
In an interesting turn of events where no one would have ever predicted selfies to become useful data, Selfiecity does a brilliant job with them. A total of 120,000 selfies were analyzed to study how people from around the world take selfies. What’s incredible about this study is how seriously it takes each and every bit of a selfie. You can find trends in almost everything from smile frequency to head tilt to pose trends to gender and more. For example, in São Paulo, women tend to take selfies with an extreme head tilt as compared to the rest of the world. In Bangkok, the selfie is all about smiles.
10. How State Populations Have Shifted
Visualization by: Washington Post
This interactive data visualization example shows the ranking of population shifts in US states for each decade since 1920. There is not one single explanation that captures the complexity of these population shifts. However, with the change in economies over the years, industries sprang up and certain states attracted migration from within the country and abroad as well. The study shows that the Southern and the Western states have experienced the biggest gains in the past century while those in the Midwest and Northeast have declined in rank.
Looking forward to a career in Data Analytics? Check out the Data Analytics Cetification Training and get certified today.
Find Hidden Stories in Data
Data visualization is beautiful. Not only does it help you visualize complex data in an easier way, but it also allows you to easily identify patterns and relationships that you never knew existed. If you want to learn more about it, you can sign up for Simplilearn’s Data Visualization Expert Master’s Program that will equip you with all the skills needed to become a data visualization expert. You will learn how to combine data, visuals, and narrative to tell impactful stories and make data-driven decisions. Get started with this course today and fast-track your career in data analytics.