"""
Repeated Choropleth Map
=======================
Three choropleths representing disjoint data from the same table.
"""
# category: geographic
import altair as alt
from vega_datasets import data
pop_eng_hur = alt.UrlData(data.population_engineers_hurricanes.url)
states = alt.UrlData(data.us_10m.url,
format=alt.TopoDataFormat(type='topojson',
feature='states'))
variable_list = ['population', 'engineers', 'hurricanes']
chart = alt.Chart(states).mark_geoshape().properties(
projection={
'type': 'albersUsa'
}, width=500, height=300).transform_lookup(
lookup='id',
from_=alt.LookupData(pop_eng_hur, 'id', variable_list)).encode(
color=alt.Color(alt.repeat('row'), type='quantitative')).repeat(
row=variable_list).resolve_scale(color='independent')
"""
London Tube Lines
=================
This example shows the London tube lines against the background of the
borough boundaries. It is based on the vega-lite example at
https://vega.github.io/vega-lite/examples/geo_layer_line_london.html.
"""
# category: geographic
import altair as alt
from vega_datasets import data
boroughs = alt.UrlData(url=data.londonBoroughs.url,
format=alt.TopoDataFormat(type='topojson',
feature='boroughs'))
centroids = data.londonCentroids.url
tubelines = alt.UrlData(url=data.londonTubeLines.url,
format=alt.TopoDataFormat(type='topojson',
feature='line'))
background = alt.Chart(boroughs).mark_geoshape(
stroke='white',
strokeWidth=2).encode(color=alt.value('#eee'), ).properties(width=700,
height=500)
labels = alt.Chart(centroids).mark_text().encode(
longitude='cx:Q',
latitude='cy:Q',
text='bLabel:N',
size=alt.value(8),
opacity=alt.value(0.6)
"""
Choropleth Map
==============
A choropleth map of unemployment rate per county in the US
"""
# category: geographic
import altair as alt
from vega_datasets import data
unemp_data = alt.UrlData(data.unemployment.url)
counties = alt.UrlData(data.us_10m.url,
format=alt.TopoDataFormat(type='topojson',
feature='counties'))
chart = alt.Chart(counties).mark_geoshape().properties(
projection={
'type': 'albersUsa'
}, width=500, height=300).encode(color='rate:Q').transform_lookup(
lookup='id', from_=alt.LookupData(unemp_data, 'id', ['rate']))
def geography(self):
return alt.InlineData(
values=util.get_geojson_resource('municipalities.topojson'),
format=alt.TopoDataFormat(type='topojson',
feature='municipalities'))
)
shape_url = "https://raw.githubusercontent.com/austinorr/ca_metrics/master/data/ca-counties.json"
columns = [
"grocery_pct",
"gas_pct",
"bank_pct",
"doc_pct",
"dent_pct",
"hair_pct",
]
CA_map = (alt.Chart(
alt.Data(url=shape_url,
format=alt.TopoDataFormat(
feature="data",
type="topojson"))).mark_geoshape(stroke="white").encode(
color=alt.condition(
selector_map,
alt.Color("properties.region:N",
scale=alt.Scale(scheme="category20")),
alt.value("lightgray"),
),
tooltip=[
alt.Tooltip("properties.name:N", title="County"),
alt.Tooltip("properties.region:N", title="Region"),
],
).add_selection(selector_map).properties(width=500,
height=800))
bar_chart = (alt.Chart(
