Inspiration:
Today's breaking global news connects us to all kinds of disasters: extreme weather of all kinds (hurricanes, tornadoes, avalanches, ice storms, severe thunderstorms, tidal surges and droughts). Although these disturbances are natural, they are disasters in the eyes of humans. Therefore we would like to use the recent Storm Data to analyze their impact on human life and economy and how to effectively reduce the harm caused by Storms.
What it does:
This project estimates the personal injuries and damage by storm through a well-informed data analysis.
How we built it:
Tableau: Used for graph generation by importing the given .csv files Excel: Used for data manipulation such as rearranging YEAR_MONTH into 2 separate files for year and month Python: Used for performing statistical computations and evaluating possible correlations
Challenges we ran into:
- When first given the datasets, one issue we ran into was being able to plan a rigid structure for our project development course from scratch.
- Deciding how to distribute tasks among group members in a way that made everyone's workload equal.
- Overcoming the the barrier of having varying levels of experience.
- Figuring out a UI that would allow both written info and interactive data visualizations to be displayed.
- Navigating new software and websites to maximize the impact of our presentation.
Accomplishments that we're proud of:
- Getting a working, interactive GitHub page that displays data in interesting ways.
- Being able to collaborate effectively to divide and conquer numerous challenges.
- Combining learnt past skills with new skills acquired during the hackathon to create a meaningful resource.
What we learned
- How to use GitHub for project management.
- How to use Tableau to create an array of different data graphics.
- How to analyze and appreciate the value of collected datasets.
What's next for estimating personal injuries and storm damage?
Recognizing the types of instances and circumstance that tend to feature a high frequency of fatal injuries and combining such trends with demographic elements allows one to predict the degree of storm damage they are likely to suffer given their US state location, physical identity, current activity, and timestamp.
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