Connections Among U.S. Airports Interactive

This example shows all the connections between major U.S. airports. Lookup transformations are used to find the coordinates of each airport and connecting airports. Connections are displayed on pointerover via a single selection.

Method syntax

import altair as alt
from vega_datasets import data

# Since these data are each more than 5,000 rows we'll import from the URLs
airports = data.airports.url
flights_airport = data.flights_airport.url

states = alt.topo_feature(data.us_10m.url, feature="states")

# Create pointerover selection
select_city = alt.selection_point(
    on="pointerover", nearest=True, fields=["origin"], empty=False
)

# Define which attributes to lookup from airports.csv
lookup_data = alt.LookupData(
    airports, key="iata", fields=["state", "latitude", "longitude"]
)

background = alt.Chart(states).mark_geoshape(
    fill="lightgray",
    stroke="white"
).properties(
    width=750,
    height=500
).project("albersUsa")

connections = alt.Chart(flights_airport).mark_rule(opacity=0.35).encode(
    latitude="latitude:Q",
    longitude="longitude:Q",
    latitude2="lat2:Q",
    longitude2="lon2:Q"
).transform_lookup(
    lookup="origin",
    from_=lookup_data
).transform_lookup(
    lookup="destination",
    from_=lookup_data,
    as_=["state", "lat2", "lon2"]
).transform_filter(
    select_city
)

points = alt.Chart(flights_airport).mark_circle().encode(
    latitude="latitude:Q",
    longitude="longitude:Q",
    size=alt.Size("routes:Q").legend(None).scale(range=[0, 1000]),
    order=alt.Order("routes:Q").sort("descending"),
    tooltip=["origin:N", "routes:Q"]
).transform_aggregate(
    routes="count()",
    groupby=["origin"]
).transform_lookup(
    lookup="origin",
    from_=lookup_data
).transform_filter(
    (alt.datum.state != "PR") & (alt.datum.state != "VI")
).add_params(
    select_city
)

(background + connections + points).configure_view(stroke=None)

Attribute syntax

import altair as alt
from vega_datasets import data

# Since these data are each more than 5,000 rows we'll import from the URLs
airports = data.airports.url
flights_airport = data.flights_airport.url

states = alt.topo_feature(data.us_10m.url, feature="states")

# Create pointerover selection
select_city = alt.selection_point(
    on="pointerover", nearest=True, fields=["origin"], empty=False
)

# Define which attributes to lookup from airports.csv
lookup_data = alt.LookupData(
    airports, key="iata", fields=["state", "latitude", "longitude"]
)

background = alt.Chart(states).mark_geoshape(
    fill="lightgray",
    stroke="white"
).properties(
    width=750,
    height=500
).project("albersUsa")

connections = alt.Chart(flights_airport).mark_rule(opacity=0.35).encode(
    latitude="latitude:Q",
    longitude="longitude:Q",
    latitude2="lat2:Q",
    longitude2="lon2:Q"
).transform_lookup(
    lookup="origin",
    from_=lookup_data
).transform_lookup(
    lookup="destination",
    from_=lookup_data,
    as_=["state", "lat2", "lon2"]
).transform_filter(
    select_city
)

points = alt.Chart(flights_airport).mark_circle().encode(
    latitude="latitude:Q",
    longitude="longitude:Q",
    size=alt.Size("routes:Q", scale=alt.Scale(range=[0, 1000]), legend=None),
    order=alt.Order("routes:Q", sort="descending"),
    tooltip=["origin:N", "routes:Q"]
).transform_aggregate(
    routes="count()",
    groupby=["origin"]
).transform_lookup(
    lookup="origin",
    from_=lookup_data
).transform_filter(
    (alt.datum.state != "PR") & (alt.datum.state != "VI")
).add_params(
    select_city
)

(background + connections + points).configure_view(stroke=None)