Let's describe what's going on here.
The map plots cities (N = 2,562) in the U.S. Each city is represented by a bubble. The color of the bubble ranges from purple to green, encoding the percentile ranking based on the amount of credit card debt that was paid down by consumers. Purple represents 1st percentile, the lowest amount of paydown while green represents 99th percentile, the highest amount of paydown.
The bubble size is coding exactly the same data, apparently in a coarser gradation. The more purple the color, the smaller the bubble. The more green the color, the larger the bubble.
The design decisions are baffling.
Purple is more noticeable than the green, but signifies the less important cities, with the least paydown.
With over 2,500 bubbles crowding onto the map, over-plotting is inevitable. The purple bubbles are printed last, dominating the attention but those are the least important cities (1st percentile). The green bubbles, despite being larger, lie underneath the smaller, purple bubbles.
What might be the message of this chart? Our best guess is: the map explores the regional variation in the paydown rate of credit card debt.
The analyst provides all the data beneath the map.
From this table, we learn that the ranking is not based on total amount of debt paydown, but the amount of paydown per household in each city (last column). That makes sense.
Shouldn't it be ranked by the paydown rate instead of the per-household number? Divide the "Total Credit Card Paydown by City" by "Total Credit Card Debt Q1 2018" should yield the paydown rate. Surprise! This formula yields a column entirely consisting of 4.16%.
What does this mean? They applied the national paydown rate of 4.16% to every one of 2,562 cities in the country. If they had plotted the paydown rate, every city would attain the same color. To create "variability," they plotted the per-household debt paydown amount. Said differently, the color scale encodes not credit card paydown as asserted but number of households by city.
This is an example of a QDV chart, in which there isn't a clear question, there is almost no data, and the visual contains several flaws. (See our Trifecta checkup guide.) We are presented 2,562 ways of saying the same thing: 4.16%.