Background

After learning that Hurricane Ian is Florida’s deadliest storm since 1935, I wanted to take a closer look at natural disasters that have occurred in the United States. With natural disasters such as wildfires, hurricanes, and tornadoes occurring year after year, I was interested in learning more about these incidents. Through my visualizations, I wanted to better understand which natural disasters are most common across the U.S., the most common natural disaster for each state, how long these incidents typically lasted, and visualize the impact of the COVID-19 pandemic as a natural disaster.

Materials

Dataset:

I used the U.S. Natural Disaster Declarations dataset from Kaggle. This dataset includes data from the Federal Emergency Management Agency (FEMA) from 1953 to 2022.

Software:

I used Microsoft Excel to clean up and refine my data and Tableau Public to create my visualizations.

Methods

Selecting and Refining Data:

I chose the U.S. Natural Disaster Declarations dataset because I thought that this data contained a lot of interesting columns that I wanted to dive into, such as the Incident Type classification, the states where the incidents occurred, and which dates the incidents began and ended. I downloaded this file as a CSV and used Excel to refine it.

Data Visualization:

I like that all the natural disasters could be grouped via the Incident Type column, and wanted to produce a visualization that portrayed the most common natural disasters that occurred across the United States. I chose a tree map to visualize common natural disasters, as tree maps are handy for comparing amounts and would allow viewers to glean this information at-a-glance. I used the Count measure to tally up the number of incidents for each natural disaster, and chose a color scheme where the darkest red would signify the natural disaster that occurs the most.

Next, I wanted to create a map that showed each state’s most common natural disaster. To do so, I manipulated the data in Excel by applying filters to separate each state and applied a formula to return the mode of the Incident Type column. I created a new sheet with all the states and the corresponding Incident Type mode of that state, and added it as a new data source in Tableau. I chose the symbol map to visualize the most common natural disaster for each state, and color-coded the incident types to make it easier for viewers to see states with the same common natural disaster.

For my next visualization, I wanted to see how long natural disasters typically lasted. From previous experience, I remember that hurricanes could last anywhere from a few days to weeks and that wildfires could continue burning for months. I wanted to visualize this data, and find out what the natural disaster with the longest duration might be. I used a date operator to calculate the days between the end date and start date of an incident and stored this data in a Calculated Field called Duration. I used a bar graph to display the data, and used the Average measure for Duration to determine how long each incident lasts on average. I chose horizontal bars to make it easier to read each incident type, and set the color to orange to make it easy to compare the durations for each natural disaster.

I was interested in how COVID might be represented as a natural disaster, and decided to create a visualization that singled out all the incidents that occurred in the U.S. in 2020. I used a logical function for the Fy (Fiscal Year) Declared column which excluded all the data apart from incidents that occurred in 2020. I chose a bubble chart to visualize the impact of COVID, as all the other natural disasters dwarf in comparison to the number of biological incidents.

Results and Interpretation

From my first two visualizations, I learned that severe storms occur most frequently as natural disasters, and are followed by hurricanes and floods. It was also interesting to confirm that the southeastern coastal states were most prone to hurricanes and that drier states such as California and Arizona frequently had wildfires.

From my third visualization, I learned that incidents involving toxic substances would result in the longest duration. I didn’t expect this, though it makes sense given that cleanup could be hazardous and it could take a long time to resolve. My estimates of hurricanes lasting a few weeks and wildfires lasting a few months were also confirmed through this visualization.

I was pretty shocked to learn from my last visualization that there were 7,855 biological incidents in 2020, which refers to the COVID pandemic. The number of biological incidents were much greater than any other incident throughout 2020, with only 858 incidents for the second-highest category of hurricanes.

Reflection

Visualizing natural disasters across the United States was very informative for me, and I helped me gain a better understanding of which natural disasters were most common and where they occurred. Using Tableau to create my visualizations was a good experience and I learned a lot about how to best utilize this tool and to create my own calculated fields.

With the pandemic having such a huge impact, this created a limitation for me by skewing my data, especially with my visualization of the most common natural disasters. Since there were so many biological incidents across the country in 2020, biological incidents ended up being the 4th most common incident type despite most of the data coming from one pandemic. If I were to continue working on this project, it would be interesting to dive deeper on one incident type, such as biological incidents and visualizing all the counties where incidents were recorded. With climate change increasing the frequency and intensity of extreme weather, it would also be interesting to look at this data through the lens of climate change, and take a look at how and where natural disasters may have increased as a result.