MSDA 607 Final Project

Motivation

Should Travelers Avoid Flying Airlines That Have Had Crashes in the Past?

The data set contains crash data from many airlines. After a crash people move to other airlines for safety. Does that mean than other airlines are more safe than the airlines that crashed? We will take a look at historical data to find if there is a pattern after the crash or is it just a fear

Obtain Data

Data Source

Parameters
- airline: Airline (asterisk indicates that regional subsidiaries are included)
- avail_seat_km_per_week: Available seat kilometers flown every week
- incidents_85_99: Total number of incidents, 1985–1999
- fatal_accidents_85_99: Total number of fatal accidents, 1985–1999
- fatalities_85_99: Total number of fatalities, 1985–1999
- incidents_00_14: Total number of incidents, 2000–2014
- fatal_accidents_00_14: Total number of fatal accidents, 2000–2014
- fatalities_00_14: Total number of fatalities, 2000–2014

Source: https://github.com/fivethirtyeight/data/blob/master/airline-safety/airline-safety.csv

library(tidyr)
library(dplyr)
library(tidyverse)
library(janitor)
library(kableExtra)
library(stringr)
library(ggplot2)
library(data.table)
library(formattable)

SQL Database

Data can be normalised in relation data structure. Following is the structure of the relation data. The main detail table tblAirlinesIncident contains details of the incident and is related to master tables tblAirlines, tblIncidentTypeMst and tblYearRange using foreign key.

View SQL Queries: https://github.com/monuchacko/cuny_msds/blob/master/data_607/DATA607_FinalProject.sql

SQL Data

MongoDB

Data can be stored in scalable document sets in MongoDB. Every row is stored in json format as document. This approach can make this data scalable. Here we are inserting document for each row. For every row MongoDB creates an identifier _id. Airlines incident data is stored with all the fields and corresponding value.

SQL MongoDB

CSV data

Data can be extracted from CSV to data frame using R packages. This data can be cleaned and transformed. In the code below we are loading data directly from the source. The data is inspected and cleaned.

# Read from csv
dsAL <- read.csv(file="https://raw.githubusercontent.com/fivethirtyeight/data/master/airline-safety/airline-safety.csv", header=TRUE, sep=",", stringsAsFactors=FALSE)

# Display sample data
head(dsAL) %>% kable() %>% kable_styling()

airline	avail_seat_km_per_week	incidents_85_99	fatal_accidents_85_99	fatalities_85_99	incidents_00_14	fatal_accidents_00_14	fatalities_00_14
Aer Lingus	320906734	2	0	0	0	0	0
Aeroflot*	1197672318	76	14	128	6	1	88
Aerolineas Argentinas	385803648	6	0	0	1	0	0
Aeromexico*	596871813	3	1	64	5	0	0
Air Canada	1865253802	2	0	0	2	0	0
Air France	3004002661	14	4	79	6	2	337

Scrub Data

Extraction/ Transformation

# Remove asterisk
dsAL$airline <- stringr::str_replace(dsAL$airline, '\\*', '')

# Use this data to populate sql master tables
# Airlines Master
airlineMst <- unique(dsAL$airline)
head(airlineMst) %>% kable() %>% kable_styling()

x
Aer Lingus
Aeroflot
Aerolineas Argentinas
Aeromexico
Air Canada
Air France

# Incident Master
incidentMst <- c("incident", "accident", "fatalities")
head(incidentMst) %>% kable() %>% kable_styling()

x
incident
accident
fatalities

# Year Range Master
yearRangeMst <- c("yr_85_99", "yr_00_14")

# View Data
head(yearRangeMst) %>% kable() %>% kable_styling()

x
yr_85_99
yr_00_14

str(dsAL)

## 'data.frame':    56 obs. of  8 variables:
##  $ airline               : chr  "Aer Lingus" "Aeroflot" "Aerolineas Argentinas" "Aeromexico" ...
##  $ avail_seat_km_per_week: num  3.21e+08 1.20e+09 3.86e+08 5.97e+08 1.87e+09 ...
##  $ incidents_85_99       : int  2 76 6 3 2 14 2 3 5 7 ...
##  $ fatal_accidents_85_99 : int  0 14 0 1 0 4 1 0 0 2 ...
##  $ fatalities_85_99      : int  0 128 0 64 0 79 329 0 0 50 ...
##  $ incidents_00_14       : int  0 6 1 5 2 6 4 5 5 4 ...
##  $ fatal_accidents_00_14 : int  0 1 0 0 0 2 1 1 1 0 ...
##  $ fatalities_00_14      : int  0 88 0 0 0 337 158 7 88 0 ...

# Rename column names (if necessary)
names(dsAL) <- c("airline", "avail_seat_km_per_week", "incidents_85_99", "fatal_accidents_85_99", "fatalities_85_99", "incidents_00_14", "fatal_accidents_00_14", "fatalities_00_14")

# Gather Data
dsALTransform01 <- gather(dsAL, "incident", "count", incidents_85_99)
dsALTransform02 <- gather(dsAL, "incident", "count", fatal_accidents_85_99)
dsALTransform03 <- gather(dsAL, "incident", "count", fatalities_85_99)
dsALTransform04 <- gather(dsAL, "incident", "count", incidents_00_14)
dsALTransform05 <- gather(dsAL, "incident", "count", fatal_accidents_00_14)
dsALTransform06 <- gather(dsAL, "incident", "count", fatalities_00_14)

# Extract Subset
dsALTransform01 <- subset(dsALTransform01, select = c(airline,avail_seat_km_per_week,incident,count) )
dsALTransform02 <- subset(dsALTransform02, select = c(airline,avail_seat_km_per_week,incident,count) )
dsALTransform03 <- subset(dsALTransform03, select = c(airline,avail_seat_km_per_week,incident,count) )
dsALTransform04 <- subset(dsALTransform04, select = c(airline,avail_seat_km_per_week,incident,count) )
dsALTransform05 <- subset(dsALTransform05, select = c(airline,avail_seat_km_per_week,incident,count) )
dsALTransform06 <- subset(dsALTransform06, select = c(airline,avail_seat_km_per_week,incident,count) )

# View Column Names of one sample DataSet
names(dsALTransform01)

## [1] "airline"                "avail_seat_km_per_week"
## [3] "incident"               "count"

# Combine Data into one DataSet
dsALTransformCombined <- rbind(dsALTransform01, dsALTransform02, dsALTransform03, dsALTransform04, dsALTransform05, dsALTransform06)

# Find the mean
al_seats_mean = mean(dsALTransformCombined$avail_seat_km_per_week)
al_seats_mean

## [1] 1384621305

# Create Columns from Existing
dsALTransformCombined <- dsALTransformCombined %>% 
  mutate(incident_type = ifelse(incident == "incidents_85_99", "incident", ifelse(incident == "fatal_accidents_85_99", "fatal_accident", ifelse(incident == "fatalities_85_99", "fatalities", ifelse(incident == "incidents_00_14", "incident", ifelse(incident == "fatal_accidents_00_14", "fatal_accident", ifelse(incident == "fatalities_00_14", "fatalities", ""))))))) %>% 
  mutate(incident_year = ifelse(incident == "incidents_85_99", "1999", ifelse(incident == "fatal_accidents_85_99", "1999", ifelse(incident == "fatalities_85_99", "1999", ifelse(incident == "incidents_00_14", "2014", ifelse(incident == "fatal_accidents_00_14", "2014", ifelse(incident == "fatalities_00_14", "2014", ""))))))) %>% 
  mutate(incident_ratio = ifelse(count == 0, round(avail_seat_km_per_week/1000000, 0), round((avail_seat_km_per_week/1000000)/count, 0) ))

head(dsALTransformCombined) %>% kable() %>% kable_styling()

airline	avail_seat_km_per_week	incident	count	incident_type	incident_year	incident_ratio
Aer Lingus	320906734	incidents_85_99	2	incident	1999	160
Aeroflot	1197672318	incidents_85_99	76	incident	1999	16
Aerolineas Argentinas	385803648	incidents_85_99	6	incident	1999	64
Aeromexico	596871813	incidents_85_99	3	incident	1999	199
Air Canada	1865253802	incidents_85_99	2	incident	1999	933
Air France	3004002661	incidents_85_99	14	incident	1999	215

al_mean = mean(dsALTransformCombined$count)
al_mean

## [1] 30.34524

al_sd = sd(dsALTransformCombined$count)
al_sd

## [1] 85.49267

dim(dsALTransform01)

## [1] 56  4

dim(dsALTransformCombined)

## [1] 336   7

Explore Data

ggplot(data=dsALTransformCombined, aes(x=count, y=incident_type)) + geom_point(aes(count, incident_type), color = "#FC4E07") + ylab("Incident Type") + xlab("Count") + ggtitle("Incident Type vs Count") +
  theme(plot.title = element_text(hjust = 0.5))

ggplot(data=dsALTransformCombined, aes(x=count, y=incident_year)) + geom_point(aes(count, incident_year), color = "#FC4E07") + ylab("Incident Year") + xlab("Count") + ggtitle("Incident Year vs Count") +
  theme(plot.title = element_text(hjust = 0.5))

ggplot(data=dsALTransformCombined, aes(x=count, y=incident_ratio)) + geom_line(aes(size=incident_ratio), color = "#FC4E07") + ylab("Incident Ratio") + xlab("Count") + ggtitle("Incident Ratio vs Count") +
  theme(plot.title = element_text(hjust = 0.5))

Model Data

#dsALTransformCombined_dnorm <- dnorm(dsALTransformCombined$count, mean=al_mean, sd=al_sd)
#plot(dsALTransformCombined$count, dsALTransformCombined_dnorm)

# Find the distribution
ggplot(data = dsALTransformCombined, aes(dsALTransformCombined$count)) +
  stat_function(fun = dnorm, n = 101, args = list(mean=al_mean, sd=al_sd)) + ylab("") + xlab("Count") +
  scale_y_continuous(breaks = NULL) + ggtitle("Distribution") +
  theme(plot.title = element_text(hjust = 0.5))

dsALTransformModel <- dsALTransformCombined

dsALTransformModel <- data.frame(subset(dsALTransformCombined, select = c(airline,incident_type,count)))
head(dsAL)

##                 airline avail_seat_km_per_week incidents_85_99
## 1            Aer Lingus              320906734               2
## 2              Aeroflot             1197672318              76
## 3 Aerolineas Argentinas              385803648               6
## 4            Aeromexico              596871813               3
## 5            Air Canada             1865253802               2
## 6            Air France             3004002661              14
##   fatal_accidents_85_99 fatalities_85_99 incidents_00_14
## 1                     0                0               0
## 2                    14              128               6
## 3                     0                0               1
## 4                     1               64               5
## 5                     0                0               2
## 6                     4               79               6
##   fatal_accidents_00_14 fatalities_00_14
## 1                     0                0
## 2                     1               88
## 3                     0                0
## 4                     0                0
## 5                     0                0
## 6                     2              337

Interpret Results

Airlines Safety

Passengers tend to avoid airlines that had major incident. They consider these airlines unsafe. We are going to use data to find out if this is true or based on fear. To find out we have a dataset of incidence, year the incident occurred and few other data.

After loading the data, we performed transformation. We put them in categories based on the incident type. We explored the data using the OSEMN workflow. After analyzing we did not see any pattern to suggest the airline that had major incident continued to be unsafe. The below heat map of airlines and incidence per year does not have a continuing pattern. These incidences appear to be independent. If we look at the distribution (see chart above), we cannot find a pattern of normal distribution suggesting that the incidences are not related to each other.

ggplot(dsALTransformCombined, aes(incident_year, airline)) + geom_tile(aes(fill = count), colour = "white") + scale_fill_gradient(low = "white", high = "steelblue") + theme(text = element_text(size=7), axis.text.x = element_text(angle=90, hjust=1)) + ylab("Airlines") + xlab("Incident Year") + ggtitle("Incidence per Year Heat Map ") + theme(plot.title = element_text(hjust = 0.5))

The table below shows the incidence type, year, incidence count and ratio to size for every airlines. After examining the data there is no correlation. We can therefore conclude that safety based on major airline incidence are influenced by fear rather than reality.

customGreen0 = "#DeF7E9"
customGreen = "#71CA97"
customRed = "#ff7f7f"

dsALTransformTable <- subset(dsALTransformCombined, select = c(airline,incident_type,incident_year,count,incident_ratio))

names(dsALTransformTable) <- c("Airlines","Incident Type","Year","Count","Ratio")
  
formattable(dsALTransformTable, align =c("l","c","c","c","c", "c", "c", "c", "r"), list(
  `Indicator Name` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
  `Count`= color_tile(customGreen, customRed),
  `Ratio`= color_tile(customGreen, customRed)
))

Airlines	Incident Type	Year	Count	Ratio
Aer Lingus	incident	1999	2	160
Aeroflot	incident	1999	76	16
Aerolineas Argentinas	incident	1999	6	64
Aeromexico	incident	1999	3	199
Air Canada	incident	1999	2	933
Air France	incident	1999	14	215
Air India	incident	1999	2	435
Air New Zealand	incident	1999	3	237
Alaska Airlines	incident	1999	5	193
Alitalia	incident	1999	7	100
All Nippon Airways	incident	1999	3	614
American	incident	1999	21	249
Austrian Airlines	incident	1999	1	358
Avianca	incident	1999	5	79
British Airways	incident	1999	4	795
Cathay Pacific	incident	1999	0	2582
China Airlines	incident	1999	12	68
Condor	incident	1999	2	209
COPA	incident	1999	3	183
Delta / Northwest	incident	1999	24	272
Egyptair	incident	1999	8	70
El Al	incident	1999	1	335
Ethiopian Airlines	incident	1999	25	20
Finnair	incident	1999	1	506
Garuda Indonesia	incident	1999	10	61
Gulf Air	incident	1999	1	301
Hawaiian Airlines	incident	1999	0	494
Iberia	incident	1999	4	293
Japan Airlines	incident	1999	3	525
Kenya Airways	incident	1999	2	139
KLM	incident	1999	7	268
Korean Air	incident	1999	12	145
LAN Airlines	incident	1999	3	334
Lufthansa	incident	1999	6	571
Malaysia Airlines	incident	1999	3	346
Pakistan International	incident	1999	8	44
Philippine Airlines	incident	1999	7	59
Qantas	incident	1999	1	1917
Royal Air Maroc	incident	1999	5	59
SAS	incident	1999	5	137
Saudi Arabian	incident	1999	7	123
Singapore Airlines	incident	1999	2	1188
South African	incident	1999	2	326
Southwest Airlines	incident	1999	1	3277
Sri Lankan / AirLanka	incident	1999	2	163
SWISS	incident	1999	2	396
TACA	incident	1999	3	86
TAM	incident	1999	8	189
TAP - Air Portugal	incident	1999	0	619
Thai Airways	incident	1999	8	213
Turkish Airlines	incident	1999	8	243
United / Continental	incident	1999	19	376
US Airways / America West	incident	1999	16	153
Vietnam Airlines	incident	1999	7	89
Virgin Atlantic	incident	1999	1	1005
Xiamen Airlines	incident	1999	9	48
Aer Lingus	fatal_accident	1999	0	321
Aeroflot	fatal_accident	1999	14	86
Aerolineas Argentinas	fatal_accident	1999	0	386
Aeromexico	fatal_accident	1999	1	597
Air Canada	fatal_accident	1999	0	1865
Air France	fatal_accident	1999	4	751
Air India	fatal_accident	1999	1	869
Air New Zealand	fatal_accident	1999	0	710
Alaska Airlines	fatal_accident	1999	0	965
Alitalia	fatal_accident	1999	2	349
All Nippon Airways	fatal_accident	1999	1	1841
American	fatal_accident	1999	5	1046
Austrian Airlines	fatal_accident	1999	0	358
Avianca	fatal_accident	1999	3	132
British Airways	fatal_accident	1999	0	3180
Cathay Pacific	fatal_accident	1999	0	2582
China Airlines	fatal_accident	1999	6	136
Condor	fatal_accident	1999	1	418
COPA	fatal_accident	1999	1	550
Delta / Northwest	fatal_accident	1999	12	544
Egyptair	fatal_accident	1999	3	186
El Al	fatal_accident	1999	1	335
Ethiopian Airlines	fatal_accident	1999	5	98
Finnair	fatal_accident	1999	0	506
Garuda Indonesia	fatal_accident	1999	3	204
Gulf Air	fatal_accident	1999	0	301
Hawaiian Airlines	fatal_accident	1999	0	494
Iberia	fatal_accident	1999	1	1173
Japan Airlines	fatal_accident	1999	1	1574
Kenya Airways	fatal_accident	1999	0	277
KLM	fatal_accident	1999	1	1875
Korean Air	fatal_accident	1999	5	347
LAN Airlines	fatal_accident	1999	2	501
Lufthansa	fatal_accident	1999	1	3427
Malaysia Airlines	fatal_accident	1999	1	1039
Pakistan International	fatal_accident	1999	3	116
Philippine Airlines	fatal_accident	1999	4	103
Qantas	fatal_accident	1999	0	1917
Royal Air Maroc	fatal_accident	1999	3	99
SAS	fatal_accident	1999	0	683
Saudi Arabian	fatal_accident	1999	2	430
Singapore Airlines	fatal_accident	1999	2	1188
South African	fatal_accident	1999	1	652
Southwest Airlines	fatal_accident	1999	0	3277
Sri Lankan / AirLanka	fatal_accident	1999	1	326
SWISS	fatal_accident	1999	1	793
TACA	fatal_accident	1999	1	259
TAM	fatal_accident	1999	3	503
TAP - Air Portugal	fatal_accident	1999	0	619
Thai Airways	fatal_accident	1999	4	426
Turkish Airlines	fatal_accident	1999	3	649
United / Continental	fatal_accident	1999	8	892
US Airways / America West	fatal_accident	1999	7	351
Vietnam Airlines	fatal_accident	1999	3	208
Virgin Atlantic	fatal_accident	1999	0	1005
Xiamen Airlines	fatal_accident	1999	1	430
Aer Lingus	fatalities	1999	0	321
Aeroflot	fatalities	1999	128	9
Aerolineas Argentinas	fatalities	1999	0	386
Aeromexico	fatalities	1999	64	9
Air Canada	fatalities	1999	0	1865
Air France	fatalities	1999	79	38
Air India	fatalities	1999	329	3
Air New Zealand	fatalities	1999	0	710
Alaska Airlines	fatalities	1999	0	965
Alitalia	fatalities	1999	50	14
All Nippon Airways	fatalities	1999	1	1841
American	fatalities	1999	101	52
Austrian Airlines	fatalities	1999	0	358
Avianca	fatalities	1999	323	1
British Airways	fatalities	1999	0	3180
Cathay Pacific	fatalities	1999	0	2582
China Airlines	fatalities	1999	535	2
Condor	fatalities	1999	16	26
COPA	fatalities	1999	47	12
Delta / Northwest	fatalities	1999	407	16
Egyptair	fatalities	1999	282	2
El Al	fatalities	1999	4	84
Ethiopian Airlines	fatalities	1999	167	3
Finnair	fatalities	1999	0	506
Garuda Indonesia	fatalities	1999	260	2
Gulf Air	fatalities	1999	0	301
Hawaiian Airlines	fatalities	1999	0	494
Iberia	fatalities	1999	148	8
Japan Airlines	fatalities	1999	520	3
Kenya Airways	fatalities	1999	0	277
KLM	fatalities	1999	3	625
Korean Air	fatalities	1999	425	4
LAN Airlines	fatalities	1999	21	48
Lufthansa	fatalities	1999	2	1713
Malaysia Airlines	fatalities	1999	34	31
Pakistan International	fatalities	1999	234	1
Philippine Airlines	fatalities	1999	74	6
Qantas	fatalities	1999	0	1917
Royal Air Maroc	fatalities	1999	51	6
SAS	fatalities	1999	0	683
Saudi Arabian	fatalities	1999	313	3
Singapore Airlines	fatalities	1999	6	396
South African	fatalities	1999	159	4
Southwest Airlines	fatalities	1999	0	3277
Sri Lankan / AirLanka	fatalities	1999	14	23
SWISS	fatalities	1999	229	3
TACA	fatalities	1999	3	86
TAM	fatalities	1999	98	15
TAP - Air Portugal	fatalities	1999	0	619
Thai Airways	fatalities	1999	308	6
Turkish Airlines	fatalities	1999	64	30
United / Continental	fatalities	1999	319	22
US Airways / America West	fatalities	1999	224	11
Vietnam Airlines	fatalities	1999	171	4
Virgin Atlantic	fatalities	1999	0	1005
Xiamen Airlines	fatalities	1999	82	5
Aer Lingus	incident	2014	0	321
Aeroflot	incident	2014	6	200
Aerolineas Argentinas	incident	2014	1	386
Aeromexico	incident	2014	5	119
Air Canada	incident	2014	2	933
Air France	incident	2014	6	501
Air India	incident	2014	4	217
Air New Zealand	incident	2014	5	142
Alaska Airlines	incident	2014	5	193
Alitalia	incident	2014	4	175
All Nippon Airways	incident	2014	7	263
American	incident	2014	17	308
Austrian Airlines	incident	2014	1	358
Avianca	incident	2014	0	397
British Airways	incident	2014	6	530
Cathay Pacific	incident	2014	2	1291
China Airlines	incident	2014	2	407
Condor	incident	2014	0	418
COPA	incident	2014	0	550
Delta / Northwest	incident	2014	24	272
Egyptair	incident	2014	4	139
El Al	incident	2014	1	335
Ethiopian Airlines	incident	2014	5	98
Finnair	incident	2014	0	506
Garuda Indonesia	incident	2014	4	153
Gulf Air	incident	2014	3	100
Hawaiian Airlines	incident	2014	1	494
Iberia	incident	2014	5	235
Japan Airlines	incident	2014	0	1574
Kenya Airways	incident	2014	2	139
KLM	incident	2014	1	1875
Korean Air	incident	2014	1	1735
LAN Airlines	incident	2014	0	1002
Lufthansa	incident	2014	3	1142
Malaysia Airlines	incident	2014	3	346
Pakistan International	incident	2014	10	35
Philippine Airlines	incident	2014	2	207
Qantas	incident	2014	5	383
Royal Air Maroc	incident	2014	3	99
SAS	incident	2014	6	114
Saudi Arabian	incident	2014	11	78
Singapore Airlines	incident	2014	2	1188
South African	incident	2014	1	652
Southwest Airlines	incident	2014	8	410
Sri Lankan / AirLanka	incident	2014	4	81
SWISS	incident	2014	3	264
TACA	incident	2014	1	259
TAM	incident	2014	7	216
TAP - Air Portugal	incident	2014	0	619
Thai Airways	incident	2014	2	851
Turkish Airlines	incident	2014	8	243
United / Continental	incident	2014	14	510
US Airways / America West	incident	2014	11	223
Vietnam Airlines	incident	2014	1	625
Virgin Atlantic	incident	2014	0	1005
Xiamen Airlines	incident	2014	2	215
Aer Lingus	fatal_accident	2014	0	321
Aeroflot	fatal_accident	2014	1	1198
Aerolineas Argentinas	fatal_accident	2014	0	386
Aeromexico	fatal_accident	2014	0	597
Air Canada	fatal_accident	2014	0	1865
Air France	fatal_accident	2014	2	1502
Air India	fatal_accident	2014	1	869
Air New Zealand	fatal_accident	2014	1	710
Alaska Airlines	fatal_accident	2014	1	965
Alitalia	fatal_accident	2014	0	698
All Nippon Airways	fatal_accident	2014	0	1841
American	fatal_accident	2014	3	1743
Austrian Airlines	fatal_accident	2014	0	358
Avianca	fatal_accident	2014	0	397
British Airways	fatal_accident	2014	0	3180
Cathay Pacific	fatal_accident	2014	0	2582
China Airlines	fatal_accident	2014	1	813
Condor	fatal_accident	2014	0	418
COPA	fatal_accident	2014	0	550
Delta / Northwest	fatal_accident	2014	2	3263
Egyptair	fatal_accident	2014	1	558
El Al	fatal_accident	2014	0	335
Ethiopian Airlines	fatal_accident	2014	2	244
Finnair	fatal_accident	2014	0	506
Garuda Indonesia	fatal_accident	2014	2	307
Gulf Air	fatal_accident	2014	1	301
Hawaiian Airlines	fatal_accident	2014	0	494
Iberia	fatal_accident	2014	0	1173
Japan Airlines	fatal_accident	2014	0	1574
Kenya Airways	fatal_accident	2014	2	139
KLM	fatal_accident	2014	0	1875
Korean Air	fatal_accident	2014	0	1735
LAN Airlines	fatal_accident	2014	0	1002
Lufthansa	fatal_accident	2014	0	3427
Malaysia Airlines	fatal_accident	2014	2	520
Pakistan International	fatal_accident	2014	2	174
Philippine Airlines	fatal_accident	2014	1	413
Qantas	fatal_accident	2014	0	1917
Royal Air Maroc	fatal_accident	2014	0	296
SAS	fatal_accident	2014	1	683
Saudi Arabian	fatal_accident	2014	0	860
Singapore Airlines	fatal_accident	2014	1	2377
South African	fatal_accident	2014	0	652
Southwest Airlines	fatal_accident	2014	0	3277
Sri Lankan / AirLanka	fatal_accident	2014	0	326
SWISS	fatal_accident	2014	0	793
TACA	fatal_accident	2014	1	259
TAM	fatal_accident	2014	2	755
TAP - Air Portugal	fatal_accident	2014	0	619
Thai Airways	fatal_accident	2014	1	1703
Turkish Airlines	fatal_accident	2014	2	973
United / Continental	fatal_accident	2014	2	3570
US Airways / America West	fatal_accident	2014	2	1228
Vietnam Airlines	fatal_accident	2014	0	625
Virgin Atlantic	fatal_accident	2014	0	1005
Xiamen Airlines	fatal_accident	2014	0	430
Aer Lingus	fatalities	2014	0	321
Aeroflot	fatalities	2014	88	14
Aerolineas Argentinas	fatalities	2014	0	386
Aeromexico	fatalities	2014	0	597
Air Canada	fatalities	2014	0	1865
Air France	fatalities	2014	337	9
Air India	fatalities	2014	158	6
Air New Zealand	fatalities	2014	7	101
Alaska Airlines	fatalities	2014	88	11
Alitalia	fatalities	2014	0	698
All Nippon Airways	fatalities	2014	0	1841
American	fatalities	2014	416	13
Austrian Airlines	fatalities	2014	0	358
Avianca	fatalities	2014	0	397
British Airways	fatalities	2014	0	3180
Cathay Pacific	fatalities	2014	0	2582
China Airlines	fatalities	2014	225	4
Condor	fatalities	2014	0	418
COPA	fatalities	2014	0	550
Delta / Northwest	fatalities	2014	51	128
Egyptair	fatalities	2014	14	40
El Al	fatalities	2014	0	335
Ethiopian Airlines	fatalities	2014	92	5
Finnair	fatalities	2014	0	506
Garuda Indonesia	fatalities	2014	22	28
Gulf Air	fatalities	2014	143	2
Hawaiian Airlines	fatalities	2014	0	494
Iberia	fatalities	2014	0	1173
Japan Airlines	fatalities	2014	0	1574
Kenya Airways	fatalities	2014	283	1
KLM	fatalities	2014	0	1875
Korean Air	fatalities	2014	0	1735
LAN Airlines	fatalities	2014	0	1002
Lufthansa	fatalities	2014	0	3427
Malaysia Airlines	fatalities	2014	537	2
Pakistan International	fatalities	2014	46	8
Philippine Airlines	fatalities	2014	1	413
Qantas	fatalities	2014	0	1917
Royal Air Maroc	fatalities	2014	0	296
SAS	fatalities	2014	110	6
Saudi Arabian	fatalities	2014	0	860
Singapore Airlines	fatalities	2014	83	29
South African	fatalities	2014	0	652
Southwest Airlines	fatalities	2014	0	3277
Sri Lankan / AirLanka	fatalities	2014	0	326
SWISS	fatalities	2014	0	793
TACA	fatalities	2014	3	86
TAM	fatalities	2014	188	8
TAP - Air Portugal	fatalities	2014	0	619
Thai Airways	fatalities	2014	1	1703
Turkish Airlines	fatalities	2014	84	23
United / Continental	fatalities	2014	109	65
US Airways / America West	fatalities	2014	23	107
Vietnam Airlines	fatalities	2014	0	625
Virgin Atlantic	fatalities	2014	0	1005
Xiamen Airlines	fatalities	2014	0	430