Reader Jack S. sent over this chart (link):
The first problem readers encounter with this image is "What is MMI?" I like to think of any presentation as a set of tearout pages. Even if the image is part of a book, or part of a deck of slides, once it is published, the writer should expect readers from tearing a sheet out and passing it along. In fact, you'd love to have people pass along your work. This means that when creating a plot such as this, the designer must explain what MMI is in the footnote. Yes, on every chart even if every chart in the report deals with MMI.
MMI, I'm told, is some kind of metric of health care cost.
What a mess. They are trying to use the metaphor of "measuring one's temperature", which I suppose is cute because MMI measures health care costs.
Next, the designer chose to plot the index against the national average as opposed to the dollar amount of MMI. This presents a challenge since the thermometer does not have a natural baseline number. This is especially true on the Fahrenheit scale used in the U.S.
Then, a map is introduced to place the major cities. The bulb of each thermometer now doubles as a dot on the map. This step is mind-boggling because the city labels aren't even on the map. So if you know where these cities are, you don't need the map for guidance but if you don't know the locations, you're as hopeless as before.
How the data now gets onto the complex picture requires some deconstruction.
First, start with a bar chart of the relative index (the third column of the table shown above).
Then, chop off the parts below 85 (colored gray).
Next, identify the cities that are below the national average (i.e. index < 100) and color them blue.
To get from here to the version published, add a guiding line from each bar to the dot on the map for the corresponding city. Notice that a constant-length portion of each bar has been chopped off, and now each bar is augmented by some additional length that varies with the distance of the bar chart from the geographical location of the city as shown on the map below. For instance, Miami, which is furthest south, has the biggest distortion.
The choice of 85 as a cutoff is arbitrary and inexplicable. If we really want to create a "cutoff" of sorts, we can use 100, which represents the national average. By plotting the gap between the city index and the national index, effectively, the percent difference, we also can use the sign of the difference to indicate above/below the national average, thus saving a color.
One of the most telling signs of a failed chart is the appearance of the entire data set next to the chart. That's the essence of the self-sufficiency test.