How to access airport information?

The traffic library provides an airport database with facilitated access to runway information and apron layout.

class traffic.data.basic.airports.Airports(data=None)

An airport is accessible via its ICAO or IATA code. In case of doubt, use the search method.

The representation of an airport is based on its geographical footprint. Contours are fetched from OpenStreetMap (you need an Internet connection the first time you call it) and put in cache.

A database of major world airports is available as:

>>> from traffic.data import airports

Airports information can be accessed with attributes:

>>> airports["EHAM"].latlon  
(52.3086, 4.7639)
>>> airports["EHAM"].iata
'AMS'
>>> airports["EHAM"].name
'Amsterdam Airport Schiphol'

__getitem__(name)

Any airport can be accessed by the bracket notation.

Parameters:: name (str) – the IATA or ICAO code of the airport
Return type:: Airport

>>> from traffic.data import airports
>>> airports["EHAM"]
Airport(icao='EHAM', iata='AMS', name='Amsterdam Airport Schiphol', country='Netherlands', latitude=52.308, longitude=4.763, altitude=-11.0)

extent(extent, buffer=0.5)

Selects the subset of data inside the given extent.

Parameters:

extent (str | ShapelyMixin | tuple[float, float, float, float]) –
The parameter extent may be passed as:
- a string to query OSM Nominatim service;
- the result of an OSM Nominatim query (ShapelyMixin);
- any kind of shape (ShapelyMixin, including Airspace);
- extent in the order: (west, east, south, north)
buffer (float) – As the extent of a given shape may be a little too strict to catch elements we may expect when we look into an area, the buffer parameter (by default, 0.5 degree) helps enlarging the area of interest.

Return type:

Optional[TypeVar(G, bound= GeoDBMixin)]

This works with databases like Airways, Airports or Navaids.

>>> from traffic.data import airways
>>> airways.extent(eurofirs['LFBB'])  
  route    id   navaid   latitude    longitude
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  A25      6    GODAN    47.64       -1.96
  A25      7    TMA28    47.61       -1.935
  A25      8    NTS      47.16       -1.613
  A25      9    TIRAV    46.6        -1.391
  A25      10   LUSON    46.5        -1.351
  A25      11   OLERO    45.97       -1.15
  A25      12   MAREN    45.73       -1.062
  A25      13   ROYAN    45.67       -1.037
  A25      14   BMC      44.83       -0.7211
  A25      15   SAU      44.68       -0.1529
 ... (703 more lines)

>>> from traffic.data import airports
>>> airports.extent("Bornholm")  
 name                 country   icao      iata   latitude   longitude
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 Bodilsker Airstrip   Denmark   DK-0027   nan    55.06      15.05
 Bornholm Airport     Denmark   EKRN      RNN    55.06      14.76
 Ro Airport           Denmark   EKRR      nan    55.21      14.88

>>> from traffic.data import navaids
>>> navaids['ZUE']
Navaid('ZUE', type='NDB', latitude=30.9, longitude=20.06833333, altitude=0.0, description='ZUEITINA NDB', frequency='369.0kHz')
>>> navaids.extent('Switzerland')['ZUE']
Navaid('ZUE', type='VOR', latitude=47.59216667, longitude=8.81766667, altitude=1730.0, description='ZURICH EAST VOR-DME', frequency='110.05MHz')

search(name)

Parameters:: name (str) – refers to the IATA or ICAO code, or part of the country name, city name of full name of the airport.

>>> from traffic.data import airports
>>> airports.query('type == "large_airport"').search('Tokyo')  
  name                                 country   icao   iata   latitude   longitude
:rtype: :sphinx_autodoc_typehints_type:`\:py\:class\:\`\~traffic.data.basic.airports.Airports\``
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Narita International Airport           Japan   RJAA   NRT    35.76      140.4
  Tokyo Haneda International Airport     Japan   RJTT   HND    35.55      139.8

Jupyter representation

Airports offer special display outputs in Jupyter Notebook based on their geographic footprint.

from traffic.data import airports
airports['EHAM']

Airport

Amsterdam Airport Schiphol (Netherlands) EHAM/AMS
(52.308601, 4.76389), altitude: -11.0

OpenStreetMap representation

The ._openstreetmap() method queries information about the airport layout from OpenStreetMap thanks to the cartes library. This data is intended to be accessed through attributes corresponding to aeroway tags, e.g. apron, taxiway, parking_position, gate, etc.

If you spot any error in data, consider contributing to OpenStreetMap.

airports["EHAM"].parking_position.head()

	id_	type_	latitude	longitude	aeroway	ref
105	2059558368	node	52.309883	4.744098	parking_position	P16
106	2059558369	node	52.312014	4.744237	parking_position	P12
107	2059558370	node	52.311070	4.744194	parking_position	P14
108	2059558371	node	52.312933	4.744377	parking_position	P10
109	2066153573	node	52.296220	4.745941	parking_position	R81R

airports["EHAM"].taxiway.head()

	id_	type_	latitude	longitude	geometry	aeroway	ref	name	surface	width
399	8586291	way	52.315922	4.746750	LINESTRING (4.74667 52.31486, 4.74663 52.31527...	taxiway	NaN	N5	concrete	23
403	8586299	way	52.325770	4.740635	LINESTRING (4.73934 52.32385, 4.73941 52.32417...	taxiway	NaN	W3	concrete	23
404	8586300	way	52.320267	4.740608	LINESTRING (4.74188 52.31959, 4.74185 52.31981...	taxiway	W5	W5	concrete	23
405	8586301	way	52.316439	4.740028	LINESTRING (4.73884 52.31843, 4.73896 52.31796...	taxiway	W6	W6	concrete	23
406	8586302	way	52.311481	4.739513	LINESTRING (4.73839 52.31354, 4.73844 52.31316...	taxiway	W7	W7	concrete	23

Warning

The GeoDataFrame associated with the represented data is accessible through the .data attribute.

Altair representation

It is also possible to benefit from their Altair geographical representation.

Airports are associated with three parameters:

footprint is True by default and shows the OpenStreetMap representation;
runways is True by default and represents the runways in bold lines.

Default parameters are strokeWidth=4, stroke="black" which can be overridden if a dictionary is passed in place of the boolean;
labels is True by default and represents the runway numbers.

Labels are automatically rotated along the runway bearing.

Default parameters are baseline="middle", dy=20, fontSize=18 which can be overridden if a dictionary is passed in place of the boolean;

import altair as alt

from traffic.data import airports
from traffic.data.samples import belevingsvlucht

chart = (
    alt.layer(
        airports["EHAM"].geoencode(
            footprint=True,  # True by default
            runways=True,  # default parameters
            labels=dict(fontSize=12, fontWeight=400, dy=10),  # font adjustments
        ),
        belevingsvlucht.first(minutes=1)
        .geoencode()
        .mark_line(color="steelblue"),
        belevingsvlucht.last(minutes=6)
        .geoencode()
        .mark_line(color="orangered"),
    )
    .properties(
        width=500, height=500, title="Belevingsvlucht at Amsterdam airport"
    )
    .configure_title(
        anchor="start", font="Lato", fontSize=16, fontWeight="bold"
    )
    .configure_view(stroke=None)
)

chart

Matplotlib representation

The airport representation can also be used in Matplotlib representations with a similar approach.

import matplotlib.pyplot as plt
from cartes.crs import Amersfoort  # Official projection in the Netherlands

fig, ax = plt.subplots(
    figsize=(10, 10),
    subplot_kw=dict(projection=Amersfoort())
)
airports["EHAM"].plot(
    ax,
    footprint=True,
    runways=dict(color="#f58518"),  # update default parameters
    labels=dict(fontsize=12),
)
ax.spines["geo"].set_visible(False)

It is possible to adjust all parameters with parameters passed to the footprint (one entry per aeroway tag type), runways and labels arguments.

fig, ax = plt.subplots(
    figsize=(10, 10),
    subplot_kw=dict(projection=Amersfoort())
)
airports["EHAM"].plot(
    ax,
    footprint=dict(taxiway=dict(color="#f58518")),
    labels=dict(fontsize=12, color="crimson"),
)
ax.spines["geo"].set_visible(False)