Measures and Averages and Indexes, Oh My!

Ah... Summer in Atlanta. Sunshine, green trees, Peachtree Road Race... and pollution. I love living in Atlanta, I really do, but one thing that most non-Atlantans don’t know is that we have a real problem with poor air quality. As someone who really enjoys biking, hiking, and running, I pay a lot of attention to the Georgia Department of Natural Resources Air Quality Index. So far this year we’ve had a handful of "orange" days, but no "red" or "purple" ones - which sounds anticlimactic, but is a real improvement over a few years ago (we haven’t registered a "code purple" day since 2002).

Anyway, as I was checking the air quality forecast this week, it occurred to me that the green/yellow/orange/red/purple/maroon categorization is based on an index. This started some thinking about measures, averages and indexes.

If you’ve never thought much about indexes, they are calculated by dividing the measured value by a base or expected value and then (usually) multiplying by 100. The result is that the target value is "100".

The great thing about indexes is... they are super easy for casual users to interpret. This is the case because they remove the dependency on the user to understand and keep absolute values in their head. In the case of the Air Quality Index, it’s based on the national air quality standard for the pollutant measured. Since air quality in Georgia is primarily composed of 7 measures, it can get pretty confusing if you want to know what’s going on. To demonstrate, here’s a table of the national air quality standards that the Georgia Department of Natural Resources monitors:

Get the point? As an environmental layperson, it’s much easier for me to interpret current measurements if they are expressed with respect to the national index (100) as opposed to ppb, ug/m^3, etc. Most people don’t have a clue (nor do they care) about what a mg/m^3 or a ppb is, but they do care about their respiratory health. So, providing a common, simplified measure makes complex data oh so much more accessible to the populous. This is the power of indexes over absolute measures and average values. In Atlanta the use of indexes has unified nearly every Atlantan’s practical understanding of air quality.

What it means for you

So, when it comes to creating information applications and dashboards, if you’re presenting complex values, consider using indexes to reduce the barriers to entry for non-domain-expert users. Here are a few tips to keep in mind:

• Just as with any new metric, it needs to follow certain guidelines for good metrics.
• Get buy-in with your user group so they don’t feel like you’re pushing yet another meaningless value down their throat.
• Don’t fall into the temptation to swap indexes with historical average values. Averages represent historical measures; indexes represent performance compared to a group.

Oh yeah, lest I forget, there’s always a tradeoff. As with most things, when you simplify, you lose some resolution. The draw back is that for those who want to delve deeper into the meaning of the measure, they now have to do some researching (just as I did) to understand how the actual metrics are measured and how the index is calculated. Take this into consideration in your information design and make the absolute measures readily available through alternate views, mouse overs, or similar.