I have to admit that when I first saw some of the recent data visualisations from the likes of the Financial Times and the New York Times, I wasn't an immediate fan. That is because they were using a logarithmic scale which distorts the data. My feeling was that they should be using a population based metric to compare different territories (XX per 100,000 is common).
|Comparison of exponential data shown on a normal scale and on a logarithmic scale|
There is a general situation where it is useful to use a log scale, and that is where there is some skew in the data. For example, where there is a mix of some very high and many lower values - such as with exponentially growing data. In that situation, the scale of the higher values can obscure the lower values.
|Ten US States growth shown on a normal scale. The higher value in one state hides detail in the other states. The dashed grey lines show example exponential growth patterns.|
|Comparison of ten US states using a logarithmic scale. The trajectory lines are straightened and it is easier to see the trajectory of the states with lower values.|
I can now clearly see that Michigan's trajectory appears to be heading in a slightly worse direction than New York's. I am not concerning myself with how much farther ahead on the trajectory New York is, only the direction that they are both travelling and hence making mental forecasts about Michigan's future.
|Bar chart with a logarithmic scale - don't do this kids! The log scale removes the comparative power of the bar chart.|
Qlik Luminary, Master's Degree in Data Analytics, Stephen Redmond is a practicing Data Professional of over 20 years experience. He is author of Mastering QlikView, QlikView Server and Publisher and the QlikView for Developer's Cookbook