Liquidations – Map Visualization [Chart Review]
Maps with data represented using bubbles are all the rage these days. Add some automation to show how they grow over time and you can really capture an audience.
[source]
Update: Here is the story that accompanied this data visualization in BusinessWeek.
If you click on the source or image above you will get a larger view that is a bit easier to read. Let’s start with the map on the left, which shows a flaw in the design of these maps in terms of accuracy. Specifically, lets focus on the orange bubble in the middle of Florida. Does this large bubble represent the state or the actual region (city) within Florida that has a large number of Circuit City closings? Honestly, I don’t know. By looking at all of the states, I guess I can logically deduce that the bubbles appear in the middle of each state, meaning the value is for the entire state.
Now, look at the value for Texas and tell me with corresponding value in the legend it matches up with. It looks like it’s about the same size as the 56+ bubble, but what would California’s value be? It’s clearly the largest bubble on the map and must represent the largest value in the legend, right? Something isn’t right. Also, the smaller bubbles are nearly impossible to quickly tell apart on the map.
Another good example of a flaw is the map on the right that shows the number of KB Toys store closings. Check out the Northeast region and tell me which value (bubble size) represents Massachusetts? It’s pretty hard to tell, huh. The map in the middle is actually the easiest to read because there aren’t any states with a large number of closing leading to a large bubble.
Now let’s put this data visualization in context. These three maps appeared in a section of BusinessWeek a few weeks back. What I do like about these maps is the ability for me to quickly visualize regions or states with large values, to some extent (Northeast). For KB Toys, I can only really tell that the Northeast has a lot of stores. I don’t think there’s much in depth analysis that can be done with these static bubble maps.
Here’s where I contradict myself.
Benefit – Quickly see regions and states with large values making them the outliers.
Drawback – Can’t quickly differentiate the small bubbles from state to state and in reference to the legend.
Now a question – do you think these bubble maps are more valuable than the more traditional color shading (heat) maps?
April 7th, 2009 at 7:28 am
I like this chart ,I think it is effective, it is clean than the heatmap,and I can compare the size of bubbles easily.
However ,it depends on the data ,when one or more state ‘s area is small,but its vaule is big ,the bubbles are diffcult to distinguish.in this case ,the heatmap will be more suitable?
April 7th, 2009 at 10:14 am
Comparing bubble sizes is problematic. Comparing shades of colors is as well. Both of these work better if there are a reasonably small number (5? 7?) of discrete sizes/colors.
The bubble analogy is the dots used for representing cities on a map, with different sized dots for city populations within a certain range. Business Week obviously tried this, but a cartographer uses dots which are small compared to the distance between cities.
What’s wrong with a bar chart? We can use the actual number, not a range, and sort them by this number. A second cut might show a heat map of the states, but even this is distorted by variations in population density.
April 7th, 2009 at 11:56 pm
Jon-
Yes ,you can always use the sorted bar chart ,people can compare them easily ,you can show the accurate numbers ,but it cannot provide the geography infomatiom ,and the number of bars will be high to 50.(sorry for there may be some grammar wrongs in my english )
April 8th, 2009 at 5:03 am
[...] Liquidations – Map Visualization, Tony Rose of DSA Insights reviewed a set of charts created by Business Week (click on the image [...]
April 8th, 2009 at 6:04 am
I’ve followed up my comments here in Map Bubble Chart of Store Closings.
Using an area-based indicator for a value makes little sense when the map already uses graphical area to encode for physical area, and where the density of the value varies inversely to this physical area. The use of color fills in the map makes much more sense. The important question the chart crafter must ask is whether the values or their geographic distribution are more important to the display.