Crash Analysis
1
Introduction
2
Data sources
2.1
Motor Vehicle Crash Data
2.2
Geospatial Data
3
Data transformation
4
Missing Values
4.1
Missing Values Analysis for Motor Vehicle Crash Analysis
4.1.1
Missing values by column
4.1.2
Bar Plot to show number of missing values by Borough
4.2
Missing Value Analysis for Missing Value Patterns plot in all data of vehicles involved in a collision
4.2.1
Missing Pattern Plot Function
4.2.2
Missing Value Patterns plot for Vehicles involved in 5 vehicle collisions
4.2.3
Missing Value Patterns plot for Vehicles involved in 4 vehicle collisions
4.2.4
Missing Value Patterns plot for Vehicles involved in 3 vehicle collisions
4.2.5
Missing Value Patterns plot for Vehicles involved in 2 vehicle collisions
4.2.6
Missing Value Patterns plot for Vehicles involved in 1 vehicle collision
4.3
Final Inference from above graphs
5
Results
5.1
What time slots see the most accidents?
5.2
Which months see the most number of accidents?
5.3
What days see the most number of accidents?
5.4
Which localities are the most accident prone?
5.5
How do the boroughs with the most and least accidents compare?
5.6
Can we observe the zip codes with the most accidents on a map?
5.7
What does the accident density over NY look like?
5.8
What are leading contributors to accidents in accident-prone areas? Also, which of these factors would lead to the most fatal accidents?
5.9
What vehicle types are involved in the maximum number of fatal accidents?
5.10
Which neighborhoods have improved or deteriorated the most?
6
Interactive component
7
Conclusion
Published with bookdown
Motor Vehicle Crash Analysis
Chapter 6
Interactive component