This blog post serves as a "how-to". For a more general post about the champion tree map see here.
This is the closest I've come to a "Pimp my Viz" submission. For a moment I thought two different graphical representations of tree shape was too crowded for one dashboard, but I like them both too much. Here's how I got there.
The Data: American Forests
The nonprofit American Forests has over 700 champion tree pages- one for each tree. I used import.io, a free web extraction tool, to collect the American Forests data. This was a two step process- first use their extractor tool on their tree search page to get a table of all of the champion trees and their URLs. I then trained the extractor tool on individual tree pages and fed it the URL table to get a data set of all champion trees and their dimensions.
|Screenshot of training import.io on a tree page.|
Dynamic Custom Shapes
I made my first tree map a few months ago using Atlanta data and circles for each tree. I avoided tree shapes the first time because I thought it would look chart-junky. But this time I figured out how to make the tree shape proportional to the actual height, canopy size, and trunk width of the tree, so each image adds unique information.
|How can we display all these unique tree shapes?!|
|Canopy image for .5x height to canopy ratio.|
|Canopy image for 3.5x height to canopy ratio.|
|A sad clear cut and weird green clouds- the workbook before selecting dual axis.|
I like the tree map, but their scattered locations and small size makes it hard to really compare different trees. So I added the "tree graph bar graph" at the bottom. To make this graph, I created a dual-axis bar graph, with one series of graphs for the canopies, and on for the height. I then played with the axes settings to make the trunk stand out below the canopies.
|Tree graph before selecting dual axis|
Final touches included a highlight action from the graph to the map, a dynamic sort on the bar graph, and background color for both the map and the bar graph. I usually keep backgrounds white to keep focus on the data, but in this case I enjoyed using colors consistent with nature.