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But guess what: You can increase granularity to crashes by the hour. Figure 1-20
breaks it down. Each row represents a year, so each cell in the grid shows an
hourly time series for the corresponding month.
With the exception of a new year's spike during the midnight hour, it's hard to
make out patterns at this level because of the variability. Actually, the monthly
chart is hard to interpret, too, if you don't know what you're looking for. There
are clear patterns, though, if you aggregate, as shown in Figure 1-21. Instead
of showing values at every hour, day, or month, you can aggregate on specific
time segments to explore the distributions.
What was hard to discern, or looked like noise before, is easy to see here.
There's a small bump in the morning when people commute to work, but
most fatal crashes occur in the evening after work. As you saw in Figure 1-19,
there are more crashes during the weekend, but summed up, it's more obvi-
ous. Finally, you can see the seasonal patterns, but more clearly, with a greater
number of accidents during the summer than in the winter.
The main point is that there's value in looking at the data beyond the mean,
median, or total because those measurements tell you only a small part of
the story. A lot of the time, aggregates or values that just tell you where the
middle of a distribution is hide the interesting details that you should actually
focus on, for both decision making and storytelling.
An outlier that stands out from the crowd could be something that you need
to fix or pay special attention to. Maybe the changes over time are a signal
that something good (or bad) is happening in your system. Cycles or regular
occurrences could help you prepare for the future. However, sometimes it
isn't helpful to see so much variability; in which case you can dial back the
granularity for generalizations and distributions.
You lose this information—the juicy bits—when you step too far away from
the data.
Think of it this way: When you look back on your life, would you rather just
remember what your days were like on average, or is it the highs and the lows
that are most important? I bet it's some combination of the two.
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