What’s the Damage?

This week I’m shifting toward looking at the damage of each tornado. The dataset I’ve been working with records the total injuries, fatalities, and property loss of each tornado. It also associates a magnitude with each tornado ranging from 0 to 5. What I expect to see is the amount of damage to increase as the magnitude increases.

Starting off with the injuries I first extracted the mag and inj from the original dataset. I then used a ListLogPlot to visualize the data. Below are the plots for injuries, fatalities, and property loss.

The data for the property loss was separated into two formats. From 1950 to 1996 a number was used to represent an estimated amount of loss. Below are the conversations of those numbers as stated in the dataset documentation.

From this I needed to convert all those entries to million dollar amounts. This is because the entries from 1996 onward are recorded in millions of dollars. To make this data not looks skewed compared to the injuries and fatalities, I then divided each entry by 1 million. Below is the process in which this is done. For each of the iterations I picked the dollar loss upper bound.

From the plots there does appear to be an upward trend compared with the magnitude. To get a better understanding of this, I added each column of damage data (injuries, fatalities, and property loss), and then added each row of magnitude. This gave me the total amount of damage for each tornado magnitude.

Putting these values in a line plot clearly shows a upward slope from 0 to 3.

The magnitudes of 4 and 5, however, do not show as much of an increase as the others. In fact, 5 decreases in damage. I believe that this is because the amount of magnitude 5 tornados is much less than 3 or 4. Taking a tally of the magnitudes shows this to be correct.

As seen, there are only 88 occurrences of magnitude 5 tornados, while there are 2703 and 714 magnitude 3 and 4 tornados respectively.

Angle Distributions

As previously stated, the Tornado directions seem to mostly lean toward the Northeast direction. To quantify this trend, I decided to look at the distribution of each Tornado spatial angle. The very helpful GeoDirection function of Mathematica can analyze each of the Tornado path angles.

This function outputs an angle in degrees called the azimuth. Below is an azimuth circle that shows what direction each angle means. The 0 degrees starts at North, while an increasing angle travels clockwise around the circle. From the Northeast prediction, there should be a higher distribution of angles around the 40-60 degree mark.

Oklahoma

At first the histogram produced for Oklahoma showed a very normal distribution near this mark, however, there is also a large volume of tornadoes at the 200 degree mark.

To solve why this was the case, I tested each of the slope clusters I had created from the clustering post. The undefined/0 slope cluster is the culprit as shown below.

So I removed the cluster of Tornados with no slope from the original histogram to get a better distribution. This distribution follows the Northeast trend as I expected, with most tornadoes in between that 40-60 degree area.

Florida

I expect Florida to follow the same pattern but with less Tornadoes in general, and it does on a more normal scale.

Louisiana

Louisiana also follows this trend.

For the Future

I want to now move away from the Tornado paths and onto my other questions previously stated.