Scatter Plots on Maps in Python
How to make scatter plots on maps in Python. Scatter plots on maps highlight geographic areas and can be colored by value.
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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.
Geographical Scatter Plot with px.scatter_geo¶
Here we show the Plotly Express function px.scatter_geo for a geographical scatter plot. The size argument is used to set the size of markers from a given column of the DataFrame.
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.scatter_geo(df, locations="iso_alpha",
size="pop", # size of markers, "pop" is one of the columns of gapminder
)
fig.show()
Customize geographical scatter plot¶
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(df, locations="iso_alpha",
color="continent", # which column to use to set the color of markers
hover_name="country", # column added to hover information
size="pop", # size of markers
projection="natural earth")
fig.show()
import plotly.express as px
import geopandas as gpd
geo_df = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
px.set_mapbox_access_token(open(".mapbox_token").read())
fig = px.scatter_geo(geo_df,
lat=geo_df.geometry.y,
lon=geo_df.geometry.x,
hover_name="name")
fig.show()
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df['text'] = df['airport'] + '' + df['city'] + ', ' + df['state'] + '' + 'Arrivals: ' + df['cnt'].astype(str)
fig = go.Figure(data=go.Scattergeo(
lon = df['long'],
lat = df['lat'],
text = df['text'],
mode = 'markers',
marker_color = df['cnt'],
))
fig.update_layout(
title = 'Most trafficked US airports<br>(Hover for airport names)',
geo_scope='usa',
)
fig.show()
Styled U.S. Airports Map¶
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df['text'] = df['airport'] + '' + df['city'] + ', ' + df['state'] + '' + 'Arrivals: ' + df['cnt'].astype(str)
fig = go.Figure(data=go.Scattergeo(
locationmode = 'USA-states',
lon = df['long'],
lat = df['lat'],
text = df['text'],
mode = 'markers',
marker = dict(
size = 8,
opacity = 0.8,
reversescale = True,
autocolorscale = False,
symbol = 'square',
line = dict(
width=1,
color='rgba(102, 102, 102)'
),
colorscale = 'Blues',
cmin = 0,
color = df['cnt'],
cmax = df['cnt'].max(),
colorbar=dict(
title=dict(
text="Incoming flights<br>February 2011"
)
)
)))
fig.update_layout(
title = 'Most trafficked US airports<br>(Hover for airport names)',
geo = dict(
scope='usa',
projection_type='albers usa',
showland = True,
landcolor = "rgb(250, 250, 250)",
subunitcolor = "rgb(217, 217, 217)",
countrycolor = "rgb(217, 217, 217)",
countrywidth = 0.5,
subunitwidth = 0.5
),
)
fig.show()
North American Precipitation Map¶
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2015_06_30_precipitation.csv')
scl = [0,"rgb(150,0,90)"],[0.125,"rgb(0, 0, 200)"],[0.25,"rgb(0, 25, 255)"],\
[0.375,"rgb(0, 152, 255)"],[0.5,"rgb(44, 255, 150)"],[0.625,"rgb(151, 255, 0)"],\
[0.75,"rgb(255, 234, 0)"],[0.875,"rgb(255, 111, 0)"],[1,"rgb(255, 0, 0)"]
fig = go.Figure(data=go.Scattergeo(
lat = df['Lat'],
lon = df['Lon'],
text = df['Globvalue'].astype(str) + ' inches',
marker = dict(
color = df['Globvalue'],
colorscale = scl,
reversescale = True,
opacity = 0.7,
size = 2,
colorbar = dict(
title = dict(
side="right"
),
outlinecolor = "rgba(68, 68, 68, 0)",
ticks = "outside",
showticksuffix = "last",
dtick = 0.1
)
)
))
fig.update_layout(
geo = dict(
scope = 'north america',
showland = True,
landcolor = "rgb(212, 212, 212)",
subunitcolor = "rgb(255, 255, 255)",
countrycolor = "rgb(255, 255, 255)",
showlakes = True,
lakecolor = "rgb(255, 255, 255)",
showsubunits = True,
showcountries = True,
resolution = 50,
projection = dict(
type = 'conic conformal',
rotation_lon = -100
),
lonaxis = dict(
showgrid = True,
gridwidth = 0.5,
range= [ -140.0, -55.0 ],
dtick = 5
),
lataxis = dict (
showgrid = True,
gridwidth = 0.5,
range= [ 20.0, 60.0 ],
dtick = 5
)
),
title=dict(text='US Precipitation 06-30-2015<br>Source: <a href="http://water.weather.gov/precip/">NOAA</a>'),
)
fig.show()
Reference¶
See function reference for px.(scatter_geo) or https://plotly.com/python/reference/scattergeo/ and https://plotly.com/python/reference/layout/geo/ for more information and chart attribute options!
What About Dash?¶
Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.
Learn about how to install Dash at https://dash.plot.ly/installation.
Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:
import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )
from dash import Dash, dcc, html
app = Dash()
app.layout = html.Div([
dcc.Graph(figure=fig)
])
app.run(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter
