The US airports with most flight routes

This post was kindly contributed by SAS ANALYSIS - go there to comment and to read the full post.

The summer just begins and a lot of people start to experience flight delay. I am also interested in seeing which cites in the US are well connected by flights. The OpenFlights project provides free flight information. Then I used R to download the data and loaded them into a SQLite database, since I try to keep them as the persistent data. RSQLite facilities R to join and query tables in the database. Finally the flight routes and the airports were visualized by ggplot2 and ggmap.

Rank	IATA code	City	Arriving flight routes
1	ORD	Chicago	494
2	LAX	Los Angeles	438
3	DEN	Denver	401
4	JFK	New York	363
5	ATL	Atlanta	339
6	DFW	Dallas-Fort Worth	281
7	SFO	San Francisco	259
8	IAH	Houston	244
9	MIA	Miami	244
10	EWR	Newark	242

Wiki says that Atlanta is the busiest airport in the US according to total passenger boardings. However, from the number of the incoming flight routs, it only ranks 5th, following Chicago, Los Angeles, Denver and New York. Possibly Atlanta is the hub mostly for passengers to do connection. If somebody really loves air traveling, Chicago(with ORD) and New York(with both JFK and EWR) are the two most convenient cities to stay with, because they have the most options.

This post is inspired by one post on the blog Data Science and R

# Import libraries and set up directory
library(ggmap)
library(RSQLite)
setwd("C:/Google Drive/Codes")

# Read data directly from URLs
airport <- read.csv("http://openflights.svn.sourceforge.net/viewvc/openflights/openflights/data/airports.dat", header = F)
route <- read.csv("http://openflights.svn.sourceforge.net/viewvc/openflights/openflights/data/routes.dat", header = F)

# Remove the airports without IATA codes and rename the variables
airport <- airport[airport$V5!='', c('V3', 'V4', 'V5','V7','V8','V9')]
colnames(airport) <- c("City", "Country", "IATA", "lantitude", "longitude", "altitude")
route <- route[c('V3', 'V5')]
colnames(route) <- c("Departure", "Arrival")

# Store data to SQLite database
conn <- dbConnect("SQLite", dbname = "air.db")
dbSendQuery(conn, "drop table if exists airport;")
dbWriteTable(conn, "airport", airport)  
dbSendQuery(conn, "drop table if exists route;")
dbWriteTable(conn, "route", route) 
dbDisconnect(conn)

# Manipulate data in SQLite database
conn <- dbConnect("SQLite", dbname = "air.db")
sqlcmd01 <- dbSendQuery(conn, " 
  select a.type, a.city as iata, a.frequency, b.city, b.country, b.lantitude, b.longitude
  from (select  'depart' as type, departure as city, count(departure) as frequency
  from route
    group by departure
    order by frequency desc, type) as a 
  left join airport as b on a.city = b.iata
  order by frequency desc
;")
top <- combine <- fetch(sqlcmd01, n = -1)
sqlcmd02 <- dbSendQuery(conn, " 
  select route.rowid as id, route.departure as point, airport.lantitude as lantitude, airport.longitude as longitude
  from route left join airport on route.departure = airport.iata
  union 
  select route.rowid as id, route.arrival as point, airport.lantitude as lantitude, airport.longitude as longitude
  from route left join airport on route.arrival = airport.iata
  order by id
;")
combine <- fetch(sqlcmd02, n = -1)
dbDisconnect(conn)

# Draw the flight routes and the airports on Google map
ggmap(get_googlemap(center = 'us', zoom = 4, maptype = 'roadmap'), extent = 'device') +
geom_line(data = combine, aes(x = longitude, y = lantitude, group = id), size = 0.1,
          alpha = 0.05,color = 'red4') +
geom_point(data = top, aes(x = longitude, y = lantitude, size = frequency), colour = "blue", alpha = 0.3) +
scale_size(range=c(0,15))

This post was kindly contributed by SAS ANALYSIS - go there to comment and to read the full post.

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