houstondatavis / data-jam-february-2017

Houston Hobby Airport data

For this month's data jam, we will be working with data from Monday at the Houston Hobby airport. This data is sourced from FlightAware's API.

We have the data available in csv, each in their respective directories.

Data

The data are found in the following files:

1. weather.csv: weather.csv -- Weather at the airport for the time-period

Column	Description
airport	Airport code; will be 'KHOU' for this dataset
time	Epoch time (see here for more info)
cloud_friendly	Cloudy/Clear/Etc.
cloud_altitude	Height of clouds (99999 if Clear)
cloud_type	Abbreviated Cloud description
conditions	None
pressure	barometric pressure (inHg)
temp_air	air temperature (Celsius)
temp_dewpoint	dewpoint (Celsius)
temp_relhum	relative humidity
visibility	visibility range (statute miles)
wind_friendly	e.g. 'Windy'
wind_direction	wind direction (knots); 360 means true north
wind_speed	wind speeds (knots)
wind_speed_gust	gust speeds (knots)
raw_data	METAR data used by pilots, e.g.'KIAH 250453Z 36015G22KT 10SM CLR 16/04 A2995 RMK AO2 SLP142 T01610044'

1. flights.csv: flights.csv -- Flight-specific data

Column	Description
flight_id	unique flight id string
ident	'ANA7211'
actual_ident	'UAL128'
departuretime	Epoch time (see here for more info)
arrivaltime	Epoch time (see here for more info)
origin	Origin airport code
destination	Destination airport code
aircrafttype	Aircraft type (e.g. 'B787')
meal_service	'Business: Dinner / Economy: Dinner'
seats_cabin_first	number of passenger seats avail. on flight in first class
seats_cabin_business	number of passenger seats avail. on flight in business class
seats_cabin_coach	number of passenger seats avail. on flight in coach class

1. routes.csv: flights.csv -- Flight-specific route plan data

Column	Description
flight_id	unique flight id string
order	1
name	e.g. 'KAYEX'
type	'Waypoint' -- or 'Origin Airport' or 'Reporting Point' or 'VOR-TAC (NAVAID)' or 'Destination Airport' or probably some others
latitude	e.g. 36.4875
longitude	e.g. -120.9478611

1. tracks.csv: flights.csv -- Flight-specific location tracking data

Column	Description
flight_id	unique flight id string
timestamp	Epoch time (see here for more info)
latitude	e.g. 37.63875
longitude	e.g. -122.3621
groundspeed	ground speed (knots)
altitude	altitude (hundreds of feet)
altitudeStatus	None
updateType	e.g. 'TA'
altitudeChange	e.g. 'C'

How was the data gathered?

code

Quick Load

R

make_csv_url <- function(name){
  url <- paste('https://raw.githubusercontent.com/houstondatavis/data-jam-february-2017/data-pipeline/', name, '.csv', sep='')

  return(url)
}

flights <- read.csv(make_csv_url('flights'))

routes <- read.csv(make_csv_url('routes'))

tracks <- read.csv(make_csv_url('tracks'))

weather <- read.csv(make_csv_url('weather'))

Python

# http://stackoverflow.com/questions/32400867/pandas-read-csv-from-url#answer-32400969

import pandas as pd
import io
import requests

def make_csv_url(name):
  return "https://raw.githubusercontent.com/houstondatavis/data-jam-february-2017/data-pipeline/" + name + ".csv"

flights = pd.read_csv(
  io.StringIO(
    requests.get(make_csv_url('flights')).content.decode('utf-8')
  )
)

routes = pd.read_csv(
  io.StringIO(
    requests.get(make_csv_url('routes')).content.decode('utf-8')
  )
)

tracks = pd.read_csv(
  io.StringIO(
    requests.get(make_csv_url('tracks')).content.decode('utf-8')
  )
)

weather = pd.read_csv(
  io.StringIO(
    requests.get(make_csv_url('weather')).content.decode('utf-8')
  )
)