Lines on maps
Below we show how to create geographical line plots using either Plotly Express with px.line_geo function or the lower-level go.Scattergeo object.
Base Map Configuration¶
Plotly figures made with Plotly Express px.scatter_geo, px.line_geo or px.choropleth functions or containing go.Choropleth or go.Scattergeo graph objects have a go.layout.Geo object which can be used to control the appearance of the base map onto which data is plotted.
Lines on Maps with Plotly Express¶
Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.line_geo(df, locations="iso_alpha",
color="continent", # "continent" is one of the columns of gapminder
projection="orthographic")
fig.show()
Lines on Maps from GeoPandas¶
Given a GeoPandas geo-data frame with linestring or multilinestring features, one can extra point data and use px.line_geo().
import plotly.express as px
import geopandas as gpd
import shapely.geometry
import numpy as np
import wget
# download a zipped shapefile
wget.download("https://plotly.github.io/datasets/ne_50m_rivers_lake_centerlines.zip")
# open a zipped shapefile with the zip:// pseudo-protocol
geo_df = gpd.read_file("zip://ne_50m_rivers_lake_centerlines.zip")
lats = []
lons = []
names = []
for feature, name in zip(geo_df.geometry, geo_df.name):
if isinstance(feature, shapely.geometry.linestring.LineString):
linestrings = [feature]
elif isinstance(feature, shapely.geometry.multilinestring.MultiLineString):
linestrings = feature.geoms
else:
continue
for linestring in linestrings:
x, y = linestring.xy
lats = np.append(lats, y)
lons = np.append(lons, x)
names = np.append(names, [name]*len(y))
lats = np.append(lats, None)
lons = np.append(lons, None)
names = np.append(names, None)
fig = px.line_geo(lat=lats, lon=lons, hover_name=names)
fig.show()
Lines on Maps with plotly.graph_objects¶
US Flight Paths Map¶
import plotly.graph_objects as go
import pandas as pd
df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df_airports.head()
df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')
df_flight_paths.head()
fig = go.Figure()
fig.add_trace(go.Scattergeo(
locationmode = 'USA-states',
lon = df_airports['long'],
lat = df_airports['lat'],
hoverinfo = 'text',
text = df_airports['airport'],
mode = 'markers',
marker = dict(
size = 2,
color = 'rgb(255, 0, 0)',
line = dict(
width = 3,
color = 'rgba(68, 68, 68, 0)'
)
)))
flight_paths = []
for i in range(len(df_flight_paths)):
fig.add_trace(
go.Scattergeo(
locationmode = 'USA-states',
lon = [df_flight_paths['start_lon'][i], df_flight_paths['end_lon'][i]],
lat = [df_flight_paths['start_lat'][i], df_flight_paths['end_lat'][i]],
mode = 'lines',
line = dict(width = 1,color = 'red'),
opacity = float(df_flight_paths['cnt'][i]) / float(df_flight_paths['cnt'].max()),
)
)
fig.update_layout(
title_text = 'Feb. 2011 American Airline flight paths<br>(Hover for airport names)',
showlegend = False,
geo = dict(
scope = 'north america',
projection_type = 'azimuthal equal area',
showland = True,
landcolor = 'rgb(243, 243, 243)',
countrycolor = 'rgb(204, 204, 204)',
),
)
fig.show()
Performance improvement: put many lines in the same trace¶
For very large amounts (>1000) of lines, performance may become critical. If you can relinquish setting individual line styles (e.g. opacity), you can put multiple paths into one trace. This makes the map render faster and reduces the script execution time and memory consumption.
Use None between path coordinates to create a break in the otherwise connected paths.
import plotly.graph_objects as go
import pandas as pd
df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df_airports.head()
df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')
df_flight_paths.head()
fig = go.Figure()
fig.add_trace(go.Scattergeo(
locationmode = 'USA-states',
lon = df_airports['long'],
lat = df_airports['lat'],
hoverinfo = 'text',
text = df_airports['airport'],
mode = 'markers',
marker = dict(
size = 2,
color = 'rgb(255, 0, 0)',
line = dict(
width = 3,
color = 'rgba(68, 68, 68, 0)'
)
)))
lons = []
lats = []
import numpy as np
lons = np.empty(3 * len(df_flight_paths))
lons[::3] = df_flight_paths['start_lon']
lons[1::3] = df_flight_paths['end_lon']
lons[2::3] = None
lats = np.empty(3 * len(df_flight_paths))
lats[::3] = df_flight_paths['start_lat']
lats[1::3] = df_flight_paths['end_lat']
lats[2::3] = None
fig.add_trace(
go.Scattergeo(
locationmode = 'USA-states',
lon = lons,
lat = lats,
mode = 'lines',
line = dict(width = 1,color = 'red'),
opacity = 0.5
)
)
fig.update_layout(
title_text = 'Feb. 2011 American Airline flight paths<br>(Hover for airport names)',
showlegend = False,
geo = go.layout.Geo(
scope = 'north america',
projection_type = 'azimuthal equal area',
showland = True,
landcolor = 'rgb(243, 243, 243)',
countrycolor = 'rgb(204, 204, 204)',
),
height=700,
)
fig.show()
London to NYC Great Circle¶
import plotly.graph_objects as go
fig = go.Figure(data=go.Scattergeo(
lat = [40.7127, 51.5072],
lon = [-74.0059, 0.1275],
mode = 'lines',
line = dict(width = 2, color = 'blue'),
))
fig.update_layout(
title_text = 'London to NYC Great Circle',
showlegend = False,
geo = dict(
resolution = 50,
showland = True,
showlakes = True,
landcolor = 'rgb(204, 204, 204)',
countrycolor = 'rgb(204, 204, 204)',
lakecolor = 'rgb(255, 255, 255)',
projection_type = "equirectangular",
coastlinewidth = 2,
lataxis = dict(
range = [20, 60],
showgrid = True,
dtick = 10
),
lonaxis = dict(
range = [-100, 20],
showgrid = True,
dtick = 20
),
)
)
fig.show()
Contour lines on globe¶
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/globe_contours.csv')
df.head()
scl = ['rgb(213,62,79)', 'rgb(244,109,67)', 'rgb(253,174,97)', \
'rgb(254,224,139)', 'rgb(255,255,191)', 'rgb(230,245,152)', \
'rgb(171,221,164)', 'rgb(102,194,165)', 'rgb(50,136,189)'
]
n_colors = len(scl)
fig = go.Figure()
for i, (lat, lon) in enumerate(zip(df.columns[::2], df.columns[1::2])):
fig.add_trace(go.Scattergeo(
lon = df[lon],
lat = df[lat],
mode = 'lines',
line = dict(width = 2, color = scl[i % n_colors]
)))
fig.update_layout(
title_text = 'Contour lines over globe<br>(Click and drag to rotate)',
showlegend = False,
geo = dict(
showland = True,
showcountries = True,
showocean = True,
countrywidth = 0.5,
landcolor = 'rgb(230, 145, 56)',
lakecolor = 'rgb(0, 255, 255)',
oceancolor = 'rgb(0, 255, 255)',
projection = dict(
type = 'orthographic',
rotation = dict(
lon = -100,
lat = 40,
roll = 0
)
),
lonaxis = dict(
showgrid = True,
gridcolor = 'rgb(102, 102, 102)',
gridwidth = 0.5
),
lataxis = dict(
showgrid = True,
gridcolor = 'rgb(102, 102, 102)',
gridwidth = 0.5
)
)
)
fig.show()
Reference¶
See function reference for px.(line_geo) or https://plotly.com/python/reference/scattergeo/ for more information and chart attribute options!