Spotting Differences - Data Points: Visualization That Means Something - page 265

Searching for outliers
Rather than looking for how units of data belong in certain groups, you
should also be interested in how they don't belong in groups. That is, there
will often be data points that stand out from the rest, which are called, you
guessed it, outliers . These are data points that are different from the rest
of the population. Sometimes they could be the most interesting part of
your story, or they could just be boring typos with a missing zero. either
way, you need to check them out to see what's going on. You don't want
to make a giant graphic on the premise of an outlier, only to find out later
from a diligent reader that your hard work makes no sense.
Graphic types have been designed specifically to highlight outliers, but in
my experience, nothing beats basic plots and common sense. Learn about
the context of your data, do your homework, and ask experts about the data
when you're not sure about something. Once you find the outliers, you can
use the same graphical techniques that we've used so far to highlight them
for readers: Use varied colors, provide pointers, or use thicker borders.
Now look at a simple example. Figure 7-32 shows a time series plot that
shows weather data scraped from Weather Underground (like you did in
Chapter 2, “Handling Data”), from 1980 to 2005. There are seasonal cycles
like you'd expect, but what's going on in the middle? It seems to be unusu-
ally smooth, whereas the rest of the data has some noise. This is nothing
to go crazy over, but if you happen to run weather models on this data, you
might want to know what has been estimated and what's real data.
FIGurE 7-32 estimated weather data from Weather Underground
Similarly, looking at the star charts you made that show crime, you can
see the District of Columbia stands out. You could have seen this just as
easily with a basic bar chart, as shown in Figure 7-33. Is it fair to compare
Washington, DC to the states, considering it has more of a city makeup?
You be the judge.