:orphan:
:html_theme.sidebar_secondary.remove:

.. This document is auto-generated by the altair-gallery extension. Do not modify directly.

.. _gallery_airport_connections:


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.


.. altair-plot::
    :remove-code:
    


    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)


.. tab-set::

    .. tab-item:: Method syntax
        :sync: method

        .. code:: python


            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)


    .. tab-item:: Attribute syntax
        :sync: attribute

        .. code:: python


            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)