Tornado Alley - CDC2022

Inspiration

Having closely observed Hurricane Ian which affected large parts of Florida a few weeks ago, we wanted to explore how other storms have affected parts of the country in the past. Texas is known for its large number of tornadoes, we focused on the 'Tornado Alley' which encompasses the 4 central states in the USA.

What it does

We break down the damage caused by the considered storms and explain the demographics of the people affected by them. We then mention some insights into the scale of these impacts and provide recommendations on how this can be mitigated in the future.

How we built it

We used ETL processes with Python scripts that extract, combine and clean the data provided by the National Centers for Environmental Information. We then used Tableau to visualize the data and identify trends that signify the damage caused.

Challenges we ran into

The data provided was broken down into individual files by year, which was not very convenient to work with. Thus, we had to design the Python script to automate the process to fetch and store it. The initial exploration was also a challenge since we had to spend a considerable amount of time narrowing down the focus of our presentation.

Accomplishments that we're proud of

We are proud of the visuals that we have built using Tableau. We're also proud of the focused analysis we could churn out in a limited amount of time.

What we learned

We learned new ways of implementing ETL operations with Python. We also extensively worked on Tableau Dashboard building

What's next for Tornado Alley - CDC2022

There are several other aspects of the dataset including but not limited to analysis of the impacts of events specific to heat, severe cold weather, and hail that can be explored further. We can use external datasets such as migratory patterns, insurance claims, and census data that can provide better insights.