Data Viz Revision: O’Reilly 2016 Data Science Salary Survey (Part 3)

This post is part of a series based on the data displayed in O’Reilly’s 2016 Data Science Salary Survey. Using the Data Chefs Revision Organizer as a guide, we will rethink and revise some of the visualizations featured in the report.

In this visualization, the authors are trying to show the proportion of  survey respondents based on their location in specific regions of the world:

oreilly-world-region-original

The blue circles do not depict the underlying data in this map, as they did in the visualizations from the first two posts in this series.  Instead, the blue bubbles here are merely a stylistic choice: they serve as pixels representing the world’s land mass. The numeric values are then laid on top of their corresponding regions.

It’s important to note that while all the categories are regional, the units vary. Sometimes they refer to countries (e.g., the United States, Canada), sometimes to entire continents (e.g., Africa, Asia), and sometimes to vague regional groupings (e.g. Latin America). Given the inconsistency in the data categories, it’s no surprising that the visualization is a little unclear too.

One of the problems with this visualization is that the values are represented as numbers, so the reader does not immediately notice the difference between the size of the values.  If you move back a little bit or squint your eyes until you can’t quite read the exact values, there’s nothing that immediately distinguishes the highest value (United States) and the lowest (Africa). Both appear to be white text that takes up roughly the same amount of space on a blue grid.

As I considered how to revise this map, my first thought was to try to salvage the blue bubble theme by using blue bubbles sized based on the values and placed over a geographic map.  Here’s a mockup I did using carto:

oreilly-world-region-excel-map

And here’s one I did using PowerBI:

oreilly-world-region-powerbi-map

While you can immediately see the size difference in values on these revisions, this type of map still has the same issue as the original, namely, confusion caussed by inconsistent geographic categories.  What countries constitute “Latin America,” for instance? If we assume that a number of the Caribbean island nations are part of Latin America, then it seems a little weird that the value is placed in the middle of South America.  Using another example, respondents from Iceland probably fall under Europe/non-UK, but there’s a disconnect (literally), because the  value bubble is all the way in mainland Europe.

There’s also a secondary problem that arise from the limitations of the tools I used: PowerBI and carto. If you look in my examples, the bubbles are not sized consistently.  In both tools, it’s difficult to make bubble maps in which the size of the circles accurately reflect area, not diameter.  For these reasons, I ruled out the bubble map.

Next, I considered a part/whole visualization, like the ones in part 2, but the fact that there are eight distinct categories, and some of the values are relatively small, I knew that there would be issues seeing the smaller values and their labels.

So, ultimately, I settled on this revision:

oreilly-world-region

 

 

data-chefs-viz-revision-organizer-oreilly-world-region

It’s just a simple bar chart, with values ranked from highest to lowest.  The benefit of using this simple graph, rather than the map, is that it elimiates the confusion caused by the inconsistent units of the regional categories. Now, because we don’t see every country on this chart, we don’t worry about it.

This may not be as visually appealing as the original, but, sometimes, the simplest solution is the best solution.

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