A look at Aviation Accidents through the ages
Charlie Sanders
December 13, 2016
Introduction
Everyday millions of Americans fly in planes that criss-cross the nation, putting their lives in the hands of professional pilots, mechanics and the flight controllers that plan their routes. Airplane travel is statistically one of the safest ways to travel, with REPLACE_ME the number of deaths per passenger mile on commercial airlines in the United States between 1995 and 2000 at about 3 deaths per 10 billion passenger miles traveled. ( https://en.wikipedia.org/wiki/Aviation_safety#cite_note-2 )
In this article we will explore all accidents recorded by the National Travel and Safety Board ( NTSB ) from 1962 up until present. We will look at single variables like accidents by Type of Plane, Brand of Plane, State or Country of origin and more. We will explore how two variables interact when we look at things like time of Accident and Weather conditions, weather conditions and pilot experience level. We will wrap it up by exploring how all these factors affect weather these accidents resulted in loss of life and severity of damage to the airplanes.
The data
This data looks at 79,141 flights as recorded by NTSB from 1962 onward. This dataset contains 31 variables.
What more information do we need ? If we’re going to use Make here, it would be helpful to know how many airplanes each company produced in addition how many of their engines were involved in an accident.
Univariate Plots Section
Above we see plotted all 76 thousand accidents across the globe, as well as the 75,556 in the contigous United States., and 2,731 in my home state of Texas.
Univariate Analysis
What is the structure of your dataset?
What is/are the main feature(s) of interest in your dataset?
What other features in the dataset do you think will help support your investigation into your feature(s) of interest?
Did you create any new variables from existing variables in the dataset?
Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?
Bivariate Plots Section
## [1] "Whats the best way to show correlation between categorical columns, like Purpose.Of.Flight and SeverityAsNumeric etc.."## [1] "Answer, Anova and Linear Regression"## [1] "Could you give me some suggestions on what to show correlation on, given my data set ?"## [1] "Answer, see below you got this"## [1] "What else can I correlate here, maybe Aircraft.Damage based on Geographic location ?"## [1] "Or Phase.Of.Flight and SeverityAsNumeric"## [1] "Number of accidents based on month - or season ? Problem here is we don't have non-accident numbers"## [1] "Maybe Aircraft damage as correlated to season, or severity to season"## [1] "What would 3 variables look like, PhaseOfFlight, Season and Airport ? What else"## mapping: group = group, x = long, y = lat
## geom_polygon: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity
## Warning: Removed 1054 rows containing missing values (geom_point).
Ideas * Color Phase.Of.Flight with fatal severity * bubble size Injury.SeveryityAsNumber ^ 3 , on the map # Bivariate Analysis
Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?
Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?
What was the strongest relationship you found?
as.numeric(flights$Aircraft.Damage)
Multivariate Plots Section
Multivariate Analysis
Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?
Were there any interesting or surprising interactions between features?
OPTIONAL: Did you create any models with your dataset? Discuss the strengths and limitations of your model.
Final Plots and Summary
Plot One
