Data can be very powerful, but the facts are often hidden behind numbers and text. We have evolved to process visual information; our eyes are designed to recognize shapes, contrasts and connections. Without data visualization, it is easy to get lost in a sea of number rows and columns.
To illustrate some of the most powerful examples of data visualization, I am going to use my personal data from the last week of the 2016 snowboarding season, in Whistler Backcomb Ski Resort, which was generated by RFID technology.
Ready to get inspired? Let me show you some examples of interactive and static data visualizations.
5 Data Visualization Examples
1. Visualizing Geospatial Data – Custom Mapping
This interactive visualization uses a custom map image overplayed with geospatial and time series data. It does an impressive job of introducing changes in geo location over time (aka travel history).
This piece allows you to explore runs, see where I’ve been each day of the week, and view where I haven’t been. This makes for great visual storytelling: taking a dense spider net of 203 runs and 36 lifts, and breaking it down in an easy-to-understand way.
2. Visualizing Hierarchies with Treemaps
This data visualization is a simple concept executed via a treemap. Using the same dataset, it shows the runs that I most often frequented. Not only is it an example of engaging data aggregate visualization (as you can explore the runs by mountain), it is also a fantastic way to visually comprehend hierarchies.
3. Visualizing Relationships with Network Graphs
Graph visualizations are used to visualize the relationship between the nodes in the network. In this graph visualization, you can see how some of the runs are connected to other runs.
In the treemap visualization above, you can see that I’ve used Wizard Express seven times. Why did I do that? I’ve used it because it connects me with the places I want to go.
Note that by using this graph you can understand the network of runs on the mountain without looking at the map. The relationship between the lifts and runs is highlighted here.
4. Visualizing Time Series Data with Line Charts
Even a simple line chart can tell a story. Here is the visualization of cumulative elevation change over time.
Every time I went down a run, I added a few hundred meters to the record. If we count up a running total of elevation change over six days, this adds up to an impressive 21,381 meters. That’s more than double the height of Mt. Everest, and as if I came down to Earth from the Ozone layer of the Stratosphere.
5. Going 3D with Spatial Data
Many business professionals argue against representing data in 3D – and I do agree that the third dimension is unnecessary in 3D bar- and pie-charts. It makes sense, however, to represent data about size (width, length, height) in 3D because the third dimension can add meaningful information.
This visualization highlights the highest points of every run (Y axis), how far they are from each other (X axis), and how far they are from the base of the mountain (Z axis). The points that are furthest away are smaller, to achieve a 3D look.
Without showing any numbers, you can see which runs start higher and which ones start right at the base.
When representing data in 3D, one component is frequently overlooked – the angle of the view.
If we change the angle, the data may look different. In the static image above, we use the cube as a visual cue to judge the position of each run. Let’s remove the cube and use a rotation matrix to perform rotation of coordinates X, Y and Z in space. Now viewers can see the position of each run in relation to each other.
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