Understanding the Department of Transportation FARS dataset
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.idea
bicycling
self-driving
state-outliers
state_changes
vmt_files
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DOT report.pdf
EDA.ipynb
FARS Analytical User's Manual 1975-2015.pdf
README.md
US State Abbreviations.csv
accident.csv
addressqry.py
eda.py
eda3.py
eda4.py
get_fars_data.py
tsa.py
vehicle_miles_traveled.py
vmt.csv

README.md

Analyzing DOT traffic fatality data

This project is a part of the Data Science Working Group at Code for San Francisco. Other DSWG projects can be found at the main GitHub repo.

-- Project Status: Completed

Project Intro

The Department of Transportation released a call-to-action for help analyzing their traffic fatality data after traffic fatalities spiked by 7.2% in 2015. As part of the San Francisco's data science working group, we tried to find ways to analyze the data in novel ways to help understand what caused the increase and what can be done to reduce deaths in the future.

Result

We analyzed the data using a number of different methods to try and find a causal relationship, but we were unable to find any clear links. When we went back to square one and looked at the fatalities adjusted for miles driven 2015 was no longer a clear outlier. This analysis is summarized in this blog post, and you can find the work behind it here.

Resources

Team members

  • Zachery Thomas
  • Brian Smith (project lead)
  • Mike Bridge
  • PeiDa Kuo
  • Kevin Vo
  • Collin Ross