There was a bit of a personal reason for picking week 51’s data set. I was hit by a TfL bus back in September. I went straight to the data to see what I could find about the route (easy) and the driver (impossible). I was hoping by the time December rolled around that the Q3 data would be available. Nope! Oh well.



This week I saw lots of great looking visuals, but many that displayed a simple map or a line chart lacked context. Data without context can be easily misunderstood. Context helps add clarity to your analysis. Context helps answer the question “Compared to what?”. In fact, we have entire chapter dedicated to context in our book (chapter 12).

Let’s look at a couple of ways that context could have been included with this week’s data.



If you want to view the number of injuries for a specific borough, that’s fine. But you can’t answer the question “compared to what?” without adding context. One way to do that is to use highlighting. In the example below, my viz is focused on Hounslow (red) and I’ve left all of the other boroughs in the viz for context (grey).

This helps me now see that Hounslow looks like it’s about average compared to the other boroughs.



Sometimes all you want to show is a summary number. That’s perfectly ok, as long as there is context for the number. For example, if you want to show the number of injuries year-to-date compared to the same period prior year, you can included three big numbers.

Using three big numbers allows us to answer the question “compared to what?”. We provide context in three way:

  1. Including the values for the prior year.
  2. Including the percent change for magnitude.
  3. Including color on the percent change to indicate good or bad.

These are two simple methods for adding context. There are many more in chapter 12 of our book.



This week’s data lended itself to mapping the data by borough. There’s nothing wrong with that. How often do you think about the background map? Which style should you use? How much detail should you show? Do you even need a background map?

Let’s look at four examples:

The standard Tableau map is dull and doesn’t provide much context.

A dark map with all layers removed except for streets provides much more geographical context.

A dark map with all layers removed puts the focus on the boroughs only and makes the colors pop.

A white map with all layers removed help put focus on the borough as well, but do the colors pop as much as they do with a dark background?

There are countless more mapping options you can choose from. The style you choose and the context you include is completely up to you, but keep the reader in mind. What style is going to make the data tell the story best?

Now here are this week’s favorites.



Author: Ryan Olshavsky
Link: Twitter