Air Traffic Landings Statistics-SFO Airport
Abir Chakraborty
12/06/2019
Introduction
San Francisco International Airport (IATA: SFO, ICAO: KSFO, FAA LID: SFO) is an international airport near San Bruno and Millbrae in unincorporated San Mateo County. It is 13 miles (21 km) south of downtown San Francisco, California,[4] It has flights to points throughout North America and is a major gateway to Europe and Asia.
SFO is the largest airport in the Bay Area and the second busiest in California, after LAX. In 2017, it was the seventh-busiest airport in the United States and the 24th-busiest in the world by passenger count.[5] It is the fifth-largest hub for United Airlines and functions as United’s primary transpacific gateway. It also serves as a secondary hub for Alaska Airlines. It is a major maintenance hub for United Airlines, and houses the Louis A. Turpen Aviation Museum and Library.
The airport is owned and operated by the City and County of San Francisco, despite it being located in San Mateo County. Between 1999 and 2004, the San Francisco Airport Commission operated city-owned SFO Enterprises, Inc., to oversee its business purchases and operations of ventures.
Source:- http://www.wikipedia.com
First Lets Load the Data
Data Source:- https://catalog.data.gov/dataset/air-traffic-landings-statistics
#Load Data As File
SFO_Airport_data_load<-read.csv(file=file.choose(), header=TRUE, stringsAsFactors=FALSE,strip.white=TRUE, sep=",")
#Convert to Table
SFO_Airport_data <- as.data.table(SFO_Airport_data_load)
#View Data in HTML Format
kable(head(SFO_Airport_data, n = 10), format = 'html') %>%
kable_styling(bootstrap_options = c('striped', 'hover'))| Activity.Period | Operating.Airline | Operating.Airline.IATA.Code | Published.Airline | Published.Airline.IATA.Code | GEO.Summary | GEO.Region | Landing.Aircraft.Type | Aircraft.Body.Type | Aircraft.Manufacturer | Aircraft.Model | Aircraft.Version | Landing.Count | Total.Landed.Weight |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200507 | ABX Air | GB | ABX Air | GB | Domestic | US | Freighter | Narrow Body | McDonnell Douglas | DC-9 | 30 | 40 | 4066000 |
| 200507 | ABX Air | GB | ABX Air | GB | Domestic | US | Freighter | Narrow Body | McDonnell Douglas | DC-9 | 41 | 1 | 102000 |
| 200507 | ATA Airlines | TZ | ATA Airlines | TZ | Domestic | US | Passenger | Narrow Body | Boeing | 757 | 200 | 2 | 396000 |
| 200507 | ATA Airlines | TZ | ATA Airlines | TZ | Domestic | US | Passenger | Narrow Body | Boeing | 757 | 300 | 167 | 37408000 |
| 200507 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Wide Body | Boeing | 767 | 333 | 1 | 320000 |
| 200507 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Narrow Body | Airbus | A319 | 114 | 160 | 21520000 |
| 200507 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Narrow Body | Airbus | A320 | 211 | 146 | 20761200 |
| 200507 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Narrow Body | Airbus | A321 | 211 | 2 | 343040 |
| 200507 | Air China | CA | Air China | CA | International | Asia | Passenger | Wide Body | Boeing | 747 | 400 | 23 | 14490000 |
| 200507 | Air France | AF | Air France | AF | International | Europe | Passenger | Wide Body | Boeing | 747 | 400 | 31 | 18855500 |
Lets Look how much the data is complete
# View Missing Data
missmap(SFO_Airport_data)
Data Looks quite Clean, now We need to keep only this year data, That means we safely discard anydata that that is less than 201901.
Lets discard the data and View it again(first 20 records)
# Subsettig Data(Dont Worry, I have USe DYPLR Below) for 2019 Landings only
SFO_Airport_data <- subset(x = SFO_Airport_data, SFO_Airport_data$Activity.Period >= '201901')
# View Data as a table
kable(head(SFO_Airport_data, n=20), format = 'html') %>%
kable_styling(bootstrap_options = c('striped', 'hover'))| Activity.Period | Operating.Airline | Operating.Airline.IATA.Code | Published.Airline | Published.Airline.IATA.Code | GEO.Summary | GEO.Region | Landing.Aircraft.Type | Aircraft.Body.Type | Aircraft.Manufacturer | Aircraft.Model | Aircraft.Version | Landing.Count | Total.Landed.Weight |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201901 | ABC Aerolineas S.A. de C.V. dba Interjet | 4O | ABC Aerolineas S.A. de C.V. dba Interjet | 4O | International | Mexico | Passenger | Narrow Body | Airbus | A320 |
|
51 | 7266648 |
| 201901 | Aer Lingus | EI | Aer Lingus | EI | International | Europe | Passenger | Wide Body | Airbus | A332 |
|
15 | 5956889 |
| 201901 | Aer Lingus | EI | Aer Lingus | EI | International | Europe | Passenger | Wide Body | Airbus | A333 |
|
16 | 6525680 |
| 201901 | Aeromexico | AM | Aeromexico | AM | International | Mexico | Passenger | Narrow Body | Boeing | B738 |
|
93 | 13417300 |
| 201901 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Narrow Body | Airbus | A319 |
|
14 | 1882748 |
| 201901 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Narrow Body | Airbus | A320 |
|
77 | 10949246 |
| 201901 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Narrow Body | Airbus | A321 |
|
6 | 1029120 |
| 201901 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Narrow Body | Boeing | B738 |
|
82 | 12529600 |
| 201901 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Wide Body | Boeing | B763 |
|
2 | 639800 |
| 201901 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Wide Body | Boeing | B772 |
|
1 | 492000 |
| 201901 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Wide Body | Boeing | B788 |
|
19 | 7220000 |
| 201901 | Air Canada | AC | Air Canada | AC | International | Canada | Passenger | Wide Body | Boeing | B789 |
|
38 | 16150000 |
| 201901 | Air China | CA | Air China | CA | International | Asia | Passenger | Wide Body | Boeing | B748 |
|
31 | 21328000 |
| 201901 | Air France | AF | Air France | AF | International | Europe | Passenger | Wide Body | Boeing | B773 |
|
31 | 17174000 |
| 201901 | Air India Limited | AI | Air India Limited | AI | International | Asia | Passenger | Wide Body | Boeing | B772 |
|
38 | 18696000 |
| 201901 | Air India Limited | AI | Air India Limited | AI | International | Asia | Passenger | Wide Body | Boeing | B773 |
|
1 | 554000 |
| 201901 | Air New Zealand | NZ | Air New Zealand | NZ | International | Australia / Oceania | Passenger | Wide Body | Boeing | B772 |
|
1 | 470000 |
| 201901 | Air New Zealand | NZ | Air New Zealand | NZ | International | Australia / Oceania | Passenger | Wide Body | Boeing | B773 |
|
29 | 16066000 |
| 201901 | Air Pacific Limited dba Fiji Airways | FJ | Air Pacific Limited dba Fiji Airways | FJ | International | Australia / Oceania | Passenger | Wide Body | Airbus | A332 |
|
12 | 4766393 |
| 201901 | Alaska Airlines | AS | Alaska Airlines | AS | Domestic | US | Passenger | Narrow Body | Airbus | A319 |
|
176 | 24250688 |
Did We Knew This!!!.
We Know that SFO airport is the gateway to Silion Valley, But It is amazing to see that so many Cargo Flights Touches SFO Airport, I wonder, the revenue that can be generated for SFO Airport if these cargo Flights can land in Nearby Airports where landing slots cost less.
SFO_Airport_data$Landing.Count <- as.numeric(SFO_Airport_data$Landing.Count)
#Get Cargo Data (Dont Worry, I have USe DYPLR Below)
SFO_cargo <- aggregate(SFO_Airport_data$Landing.Count, by=list(Landing.Aircraft.Type=SFO_Airport_data$Landing.Aircraft.Type), FUN=sum)
#Change the Column Names
colnames(SFO_cargo) <- c("Landing Aircraft Type", "Count")
#View HTML Data Table
kable(head(SFO_cargo, n=5), format = 'html') %>%
kable_styling(bootstrap_options = c('striped', 'hover'))| Landing Aircraft Type | Count |
|---|---|
| Freighter | 1012 |
| Passenger | 102042 |
Landing Density Distribution of SFO Airport.
This Information Can be used to Determine the how many Landings in Total takes place in SFO
#Set options
options(repr.plot.width = 8, repr.plot.height = 4, repr.plot.res = 5000) #Setting plot size
# get Landing Distribution
SFO_Airport_data%>%
ggplot(aes(Landing.Count, fill=..count..))+
geom_bar(stat = "density")+
ggtitle("Landing Distribution in SFO Airport")+
xlab('Number of Landing Distribution Per month')+
ylab('Number of Landings')
Count of Different Type of Aircraft Landings in SFO(Aircraft Manufacturer).
Clearly Airlines Fly More Boeing than Airbus Aircrafts, by almost 1.5 times
# Convert Landing Count as a Numeric
SFO_Airport_data$Landing.Count <- as.numeric(SFO_Airport_data$Landing.Count)
# Agreegate All Landing Data per Aircraft Manufacturer
SFO_Aircrafttypes <- aggregate(SFO_Airport_data$Landing.Count, by=list(Aircraft.Manufacturer=SFO_Airport_data$Aircraft.Manufacturer), FUN=sum)
# Get Color Pallet for random Color Generation
color = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
barplot(SFO_Aircrafttypes$x, main="Top 10 timezones",
xlab="Aircraft Carrier", ylab = "Number of Landings",ylim=c(0,50000),col=sample(color, nrow(SFO_Aircrafttypes)),
legend = SFO_Aircrafttypes$Aircraft.Manufacturer, beside=TRUE)
Number of Wide Body vs Narrow Body Aircraft Landing in SFO.Per Year(Ignoring Regional Jet and Turbo Prop’s)
From this Graph, We can figure out that number of Narrow Body Aircraft landing is increasing, This explains that the concept of Hub-Spoke Model is being replaced by point-point Flights Served by Narrow Body Aircraft.
# Get All Details of Landings
SFO_Airport_All <- data.frame(SFO_Airport_data_load)
# Create a Variable with Year of Landings
SFO_Airport_All$year=substr(SFO_Airport_All$Activity.Period, 1, 4)
SFO_Airport_All$Landing.Count <- as.numeric(SFO_Airport_All$Landing.Count)
SFO_Airport_All$year <- as.numeric(SFO_Airport_All$year)
# Here I use the DYPLR to Consolidate on Aircraft Body Type
SFO_aircraft_body_type_per_Year <- SFO_Airport_All%>%
group_by(year,Aircraft.Body.Type)%>%
summarise(sum_landing = sum(Landing.Count))
# Set Column Names
colnames(SFO_aircraft_body_type_per_Year) <- c("year", "Aircraft_Body_Type", "sum_landing")
# GGPLOT
ggplot(SFO_aircraft_body_type_per_Year, aes(fill=Aircraft_Body_Type, y=sum_landing, x=year)) +
geom_bar(position="dodge", stat="identity") +
theme(legend.position = 'bottom', axis.text.x=element_text(angle=45, hjust=1, size = 15),)+
ggtitle("Landing of Varous Body Types") +
xlab("Year on Year Landings")+
ylab("Number of Landings") 
Top 10 Aircraft Landings in SFO Airport for Year 2019
From this Graph, We can figure out that number of Narrow Body Aircraft landing is higher, This explains that the concept single Isle 737/A320 Family(with A319)dominates the Sky Above SFO Airport. 737 and A320 the single Aisle Workhorse for Airlines Operating into SFO Airport
# Using DYPLR to:-
# Filter Only Boeing & Airbus Aircraft(As Other are two small in Number)
# Then -> get the total of all Aircraft Model
SFO_aircraft_type <- SFO_Airport_All%>%filter(Aircraft.Manufacturer == "Boeing" | Aircraft.Manufacturer == "Airbus")%>%group_by(Aircraft.Model)%>%
summarise(sum_landing = sum(Landing.Count))%>%arrange(desc(sum_landing))%>%top_n(10)
# GGPLOT
ggplot(SFO_aircraft_type, aes(fill=Aircraft.Model, y=sum_landing, x=Aircraft.Model)) +
geom_bar(position="dodge", stat="identity") +
theme(legend.position = 'bottom', axis.text.x=element_text(angle=45, hjust=1, size = 15))+
ggtitle("Aircraft Type for top 10 Landings") +
xlab("Aircraft Type")+
ylab("Number of Landings")