Guest post from Dr. T. Alan Keahey, Visualization Expert, IBM Center for Advanced Visualization
Data visualization is a science that has made a lot of progress over the centuries, and particularly over the last 100 years.
During that time a few specific types of data visualizations have become very widely used. There have been many volumes written about how to choose the correct visualization for each task, however, I think it is also interesting to compare them to another well known technology that has evolved significantly over the last 100 years: the automobile.
Just as with choosing the right vehicle for the given route and payload, it is important to choose the right visualization “vehicle” to carry your data to the viewer:
Bar Chart = Pickup Truck: The basic bar chart is capable of conveying a wide range of data types and can do it in efficiently sized chunks. This is a working man’s visualization that can often get the job done all by itself. When in doubt, a bar chart is often a good place to start for your visualization needs.
Line Chart = Municipal Bus: Line charts are similar to bar charts, except that the horizontal axis always refers to time steps. This is similar to a bus or delivery van on a fixed schedule. They can haul a lot of the same data as a bar chart, but they do it at a fixed sequence of stopping points.
Scatter Chart = Larger Courier Truck: Scatter plots (image to the right) can show everything that bar and line charts show, but they can also show much higher volumes of data and different types of data. These are the serious haulers to use when you have a lot of data to view. Think UPS/FEDEX truck or even an 18-wheeler.
Bar/Line/Scatter Chart Matrices = Synchronized Fleet: Sometimes you just have more information than you can fit in a single chart. Combining multiple charts can let you introduce additional dimensions into your view. As with courier fleet control, you have to be careful in your planning to ensure that the charts are well synchronized to provide complete coverage without chaotic overlap.
Statistical Chart = Sporty Roadster: Specialized statistical distribution charts like the box and whiskers plot allow you to look at the basic properties of the data without having to view every single element. Like a nimble roadster these can be a bit tricky to get the most out of, but they allow you to move through the data quickly and get a good feel for the curves in the road.
Choropleth Map = Trolley Car: A choropleth is a map (below) that uses color to indicate the data values in specific regions. These are a good choice when you know that the geographical relation is a primary data property that you want to convey, just as city planners may choose a trolley car route to cover the most critical destinations.Network Diagram = Race Car: Network diagrams (see image below) can be sexy and fun to look at. They engage the viewer and draw him or her into wondering about the information within. They have a great deal of expressive power, however they also have limitations. Network diagrams are highly dependent upon the choice of the layout algorithm being used. A layout for one application may be completely inappropriate for a different application. Similarly, race cars need to be set up uniquely for different track layouts. A Formula 1 street racer would fare poorly on an oval dirt track.
Pie Chart = Horse and Buggy: The venerable pie chart does almost nothing that cannot be done better with the more efficient bar chart. Some conventional wisdom has it that pie charts should be used to show “parts of a whole,” but in most such cases a bar chart will do a better job of that. The one possible exception is when showing that one or more parts add up to more than half of the total (however, even that has restrictions). These can be nice to look at, but are terribly inefficient and a bit smelly.
3D Bar/Line/Pie Chart = Horse Drawn Automobile: In the early days of the automobile, some manufacturers created hybrid vehicles that could run on either gasoline or be drawn by horses. The result was an abomination that did poorly in either mode. There is no reason to add 3D glitz to these charts, as it will make them harder to read values. If you would like to improve the aesthetics, you should instead explore colors, shading and textures to create a more pleasing visualization.
Dials and Gauges = The Edsel: Dials and gauges are often used in data “dashboard” design to convey a feeling of being in situational control. Usually these visual elements are “all show and no go,” similar to the huge fender flares and chrome elements of the failed Edsel car from the 1950s. While some limited use of dials and gauges can be justified in special situations (such as environmental monitoring), it is important to limit their impact so that they do not overwhelm with clutter and wasted space.
There are many other types of visualizations out there to choose from, and different types of vehicles to compare. While it sometimes is possible to transport a sheet of plywood strapped to the roof of a sedan, it’s rarely the best choice. Next time you are choosing a visualization method for a data set, consider carefully if you are matching the right chart for the data and viewing task.
Continue exploring advanced visualization on IBM Many Eyes
Why stop the insight with this article? Visit IBM’s visualization hub, IBM Many Eyes and join more than 100,000 like-mined visualization enthusiasts, academia and professionals. Many Eyes v2 will launch next week with several new enhancements that continue to deliver on site’s heritage of advancing visualization, including:
· Comprehensive site redesign that includes an updated site layout and presentation. Plus, new affinity areas to find and navigate visualizations by industry or topic, such as finance, healthcare and risk.
· Addition of the Expert Eyes blog dedicated to helping you learn how to create effective and engaging visualizations that provide maximum insight and tell a story. IBM visualization luminaries and IBM Researchers from the Center for Advanced Visualization will contribute regular thought leadership and perspectives.
· New visualization options, including a heatmap and view-in-context visualization built on IBM’s Rapidly Adaptive Visualization Engine (RAVE). RAVE, a declarative language based on the IBM patented ‘Grammar-of-Graphics’ approach, provides an intuitive way to create a visualization by describing what the visualization should look like not how. With Many Eyes, RAVE does the work behind the scene and you create your visualization in three easy steps.
Discover the newest version of Many Eyes beginning March 25 by visiting ibm.com/manyeyes.
Dr. T. Alan Keahey has been a leading expert on information visualization systems for close to 20 years. His experience runs the gamut including national labs research scientist, research director at a Lucent Bell Labs spin off, founder of his own visualization R&D company and is now a member of the IBM Center for Advanced Visualization.