In 1858 the general public was familiar with seeing statistics presented in tables. Visualizations showing statistics and data were not widely used and were difficult to construct. A visualization was shown to Queen Victoria in this year and would live on and continue to be used in the 21st century.
The visualization showed the number of deaths from soldiers in the Crimean War, fought in the 1850s. Specifically, the figure showed the number of deaths that occurred from preventable diseases, wounds, and other causes.
Florence Nightingale, among many roles, was a pioneer in statistical graphics. As a nurse, Nightingale was a witness of war deaths and the medical care of soldiers. For the benefit of the general public, she synthesized vast amounts of data on hospitalizations and medical care.
Nightingale popularized the use of the polar area diagram, also called a coxcomb plot or rose diagram. Polar area diagrams are similar to pie charts, but they have identical angles and extend from the plot’s center depending on the magnitude of the values that are plotted.
Think of the polar area diagram as a pie chart meeting a histogram. A few advantages of polar area diagrams include:
- They are useful for plotting cyclical data. For example, the counts of a phenomenon in each of the 12 calendar months of a year.
- They are easy to read around the “rose” because data are presented chronologically.
- Multiple layers can be added within a diagram. This is how Nightingale separated deaths from diseases, wounds, and other causes.
In celebration of Nightingale, it’s worth thinking about how polar area diagrams might be fitting to visualize forestry data.
Polar area diagrams in forest inventories
I wanted to use polar area diagrams to answer one question: In which months do forest inventories occur across New England? A map of the US states:
I queried the US Department of Agriculture’s Forest Inventory and Analysis (FIA) database to obtain the months that forests were inventoried. The specific variable I was interested in was MEASMON, a variable contained in the PLOT table that lists the month in which the forest was inventoried.
I filtered the data to obtain the measurements of all FIA plots that were collected in their most recent measurement (generally since 2019). I also filtered the data so that at least one accessible forest land condition was present on an FIA plot.
Here is a table of the results:
| MONNAME | MEASMON | Number of measurements |
|---|---|---|
| Jan | 1 | 235 |
| Feb | 2 | 296 |
| Mar | 3 | 422 |
| Apr | 4 | 496 |
| May | 5 | 514 |
| Jun | 6 | 589 |
| Jul | 7 | 584 |
| Aug | 8 | 636 |
| Sep | 9 | 478 |
| Oct | 10 | 507 |
| Nov | 11 | 407 |
| Dec | 12 | 250 |
The result was about what you might expect for a region that has mostly temperate forests and where the majority of field work occurs in the summer months. The total number of FIA plot measurements was smallest in January (235 measurements) and largest in August (636 measurements):
The polar area diagrams using the FIA data are revealing because they show cyclical data through the calendar year and they visualize the number of plot measurements in each month. Other visualizations of these data could include a pie graph or histogram, but the polar area diagram can make each data point “pop”.
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By Matt Russell. Subscribe to our monthly email newsletter for data and analytics trends in the forest products industry. We send one email on the last Thursday of every month.