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Posts Tagged ‘hue’

Data Visualization – Size of NFL Football Players Over Time

June 5, 2014 Leave a comment
1920 height/weight of NFL players

Screenshot from http://noahveltman.com/nflplayers/ – 1920

2014 height/weight of NFL players

Screenshot from http://noahveltman.com/nflplayers/ – 2014

I love Noah Veltman’s visualization of the changing height and weight distribution of professional football players. It uses animation to convey the incredible increase in size of the typical football player, and it does so with a minimal amount of chart junk. Let’s look at two aspects that make this effective.

It uses the appropriate visualization

There are 4 variables plotted on the graph – height, weight, density, and time. Two of the variables are encoded in the axes of the chart. The time dimension is controlled by the slider (or by hitting the play button). The density is represented by the color on the chart.

You could present this data as a table of data but it would be much harder to understand the pattern that the animation conveys in a very simple manner – not only are players getting bigger in both terms of height and weight, but the variance is increasing as well.

It makes good use of color

It uses color appropriately, by varying the saturation rather than the hue. I’ve blogged about this topic before when discussing the Wind Map. To repeat my favorite quote about this, Stephen Few states in his PDF “Practical Rules for Using Color in Charts”:

When using color to encode a sequential range of quantitative values, stick with a single hue (or a small set of closely related hues) and vary intensity from pale colors for low values to increasingly darker and brighter colors for high values

Extensions

I could imagine extending this visualization in a few ways:

  • Allow users to view the players that match a given height/weight combination (who exactly are the outliers?)
  • Allow restricting the data to a given position (see how quarterbacks’ height/weight are distributed vs those of the offensive line)
  • Compare against some other normalized metrics, such as rate of injury. Is there a correlation?

This is a great data visualization because it tells a story and it spurs the imagination towards additional areas of analysis and research.

Wind Map – a visualization to make Tufte proud

June 9, 2012 2 comments

Wind map picture

Edward Tufte is a noted proponent of designing data rich visualizations. His books, including the seminal The Visual Display of Quantitative Information have influenced countless designers and engineers. When I first saw Fernanda Viégas and Martin Wattenberg’s Wind map project via Michael Kleber’s Google+ post, I immediately became entranced with it. After studying it for some time, I feel that the designers must have been intimately familiar with Tufte’s work. Let us examine how this triumph of data visualization succeeds.

Minimalist and data dense

Tufte describes the data density of charts based on the amount of information conveyed per measure of area. There are two ways of increasing data density – increasing the amount of information conveyed, and decreasing the amount of non-essential pixels in the image.

No chart junk

You’ll immediately notice what’s not in the image – there’s no compass rose, no latitude or longitude lines, or any other grid lines separating the map from the rest of the page. There aren’t even dividing lines between the states. It isn’t a map at all about political boundaries, so this extra information would only detract from the data being conveyed.

More info

This map conveys two variables, wind speed and wind direction, for thousands of points across the United States. A chart conveying the same information would take far more space and the viewer would have no way of seeing the patterns that exist.

Does not abuse color

In the hands of less restrained designers, this map would be awash in color. You see this often in weather maps and elevation maps, as illustrated below:

Snowfall example
Egregious elevation map
Egregious elevation map 2

The problem is that it is difficult to place colors in a meaningful order quickly. Yes, there is the standard ROYGBIV color ordering of the rainbow, but it’s difficult to apply quickly. Quick – what’s ‘bigger’ – orange or mauve? How about pink or green? Yellow or purple?. It is much easier to compare colors based on their saturation or intensity rather than hue. Color is great for categorical differences, but not so great for conveying quantitative information. Stephen Few sums it up nicely in his great PDF “Practical Rules for Using Color in Charts

When using color to encode a sequential range of quantitative values, stick with a single hue (or a small set of closely related hues) and vary intensity from pale colors for low values to increasingly darker and brighter colors for high values

The designers uses five shades of gray, each of which is distinguishable from the others, rather than a rainbow of colors. Five options is a nice tradeoff between granularity and ease of telling the shades apart.

Excellent use of the medium

In a print medium, the shades of gray would have had to suffice to illustrate how fast the wind was moving. In this medium, the designers used animation to illustrate the speed and direction of the wind in a truly mesmerizing way.

Conclusion

This visualization does a lot of things right. In particular, it uses a great deal of restraint in conveying the information. Unlike some of the other examples I showed, it does not have extra chart junk wasting space, it does not abuse color to try to convey quantitative information, and it is absolutely aesthetically pleasing.